Alternatives to MPCPy

Epicor Connected Process Control provides a simple-to-use software solution that allows you to configure digital work instructions and enforce process control. It also ensures that operations are error-proof. Connect IoT devices to collect 100% time studies and process data, images and images at the task level. Real-time visibility and quality control on a new level! eFlex can handle any number of product variations or thousands of parts, whether you are a component-based or model-based manufacturer. Work instructions can be linked to Bill of Materials, ensuring that products are built correctly every time, even if changes are made during the process. Work instructions that are part a system that is advanced will automatically react to model and component variations and only display the right work instructions for what's currently being built at station.

Cybernetica specializes in providing Nonlinear Model Predictive Control (NMPC) utilizing mechanistic models. Our innovative software solution, Cybernetica CENIT, features a versatile architecture capable of addressing diverse industrial challenges by delivering optimal strategies. This includes advanced multivariable optimal control, predictive control mechanisms, and intelligent feed-forward strategies, along with efficient handling of constraints. Furthermore, our adaptive control capabilities leverage state and parameter estimation, incorporating feedback from indirect measurements via the process model. The use of nonlinear models allows for effective operation across extensive ranges, enhancing the management of nonlinear processes. This leads to a diminished reliance on step-response experiments and bolstered accuracy in state and parameter estimations. Additionally, we offer control solutions for both batch and semi-batch operations, efficiently managing nonlinear processes that function under fluctuating conditions. Our technology also ensures optimal grade transitions in continuous operations, safe supervision of exothermic processes, and control of unmeasured variables, including conversion rates and product quality. As a result, we contribute to reduced energy consumption and a lower carbon footprint, while also enhancing overall process efficiency. In summary, Cybernetica is committed to advancing industrial control solutions that optimize performance and sustainability.

DigitalClone®, for Engineering is the only software that integrates multiple scales of analysis into a single package. It is the world's best gearbox reliability prediction tool. DC-E, in addition to the modeling and analysis capabilities at the level of the gearbox and the gear/bearing, is the only software that models fatigue life using detailed, physics-based models (US Patent 10474772B2). DC-E allows the construction of a digital twin of a gearbox. This includes all stages of the asset's lifecycle, from design and manufacturing optimization to supplier selection to failure root cause analysis to condition based maintenance and prognostics. This computational environment reduces the time and cost of bringing new designs to market and maintaining them over time.

A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.

The Model Predictive Control Toolbox™ offers a comprehensive suite of functions, an intuitive app, Simulink® blocks, and practical reference examples to facilitate the development of model predictive control (MPC) systems. It caters to linear challenges by enabling the creation of implicit, explicit, adaptive, and gain-scheduled MPC strategies. For more complex nonlinear scenarios, users can execute both single-stage and multi-stage nonlinear MPC. Additionally, this toolbox includes deployable optimization solvers and permits the integration of custom solvers. Users can assess the effectiveness of their controllers through closed-loop simulations in MATLAB® and Simulink environments. For applications in automated driving, the toolbox also features MISRA C®- and ISO 26262-compliant blocks and examples, allowing for a swift initiation of projects related to lane keep assist, path planning, path following, and adaptive cruise control. You have the capability to design implicit, gain-scheduled, and adaptive MPC controllers that tackle quadratic programming (QP) problems, and you can generate an explicit MPC controller derived from an implicit design. Furthermore, the toolbox supports discrete control set MPC for handling mixed-integer QP challenges, thus broadening its applicability in diverse control systems. With these extensive features, the toolbox ensures that both novice and experienced users can effectively implement advanced control strategies.

AVEVA APC is an innovative model predictive advanced process control solution designed to enhance process profitability. In the current economic landscape, manufacturers encounter challenges such as constrained capital budgets, escalating manufacturing and energy expenses, and fierce global competition. AVEVA's Comprehensive Advanced Process Control equips businesses to tackle intricate manufacturing issues with cutting-edge automatic control solutions, maximizing value extraction from their operations. This technology not only boosts production yield and quality but also minimizes energy consumption. By facilitating the optimization of manufacturing activities, it enables continuous performance enhancements crucial for improving overall financial outcomes. AVEVA APC stands out as a holistic software solution that leverages advanced technology to create automatic control systems capable of unlocking the full potential of manufacturing processes. Ultimately, this empowers manufacturers to thrive in a highly competitive market while achieving sustainable growth.

Advanced Process Control (APC) provides a highly efficient solution for enhancing your plant's performance without requiring any hardware modifications. By implementing an APC application, you can stabilize operations while simultaneously optimizing production or energy usage, leading to a deeper insight into your production processes. This term encompasses a wide array of methods and technologies that complement fundamental process control systems, which are primarily constructed using PID controllers. Some examples of APC technologies include LQR, LQC, H_infinity, neural networks, fuzzy logic, and Model-Based Predictive Control (MPC). An APC application continually optimizes plant operations every minute, round-the-clock, seven days a week, ensuring consistent efficiency. Among these technologies, MPC stands out as the most widely adopted within the industry, as it utilizes a process model to forecast the plant's behavior for the near future, typically ranging from a few minutes to several hours ahead, thus providing a strategic advantage in operational planning. Through the continual refinement of processes, APC not only improves efficiency but also contributes to long-term sustainability goals.

Enhance the precision and sustainability of Advanced Process Control (APC) models by integrating both linear and nonlinear variables through deep learning techniques, thereby expanding their operational capabilities. Achieve better return on investment by facilitating swift controller implementation, ongoing model enhancements, and more streamlined workflows that simplify the process for engineers. Transform the model development landscape with artificial intelligence and refine controller tuning via intuitive wizards that guide users in defining both linear and nonlinear optimization goals. Boost controller availability by utilizing cloud technology to access, visualize, and analyze real-time Key Performance Indicators (KPIs). In the fast-paced global market, energy and chemical industries must adapt with agility to satisfy consumer demands and optimize profit margins. Aspen DMC3 represents cutting-edge digital technology that empowers companies to realize a 2-5% increase in throughput, a 3% enhancement in yield, and a 10% decrease in energy consumption. Explore the innovative advancements in next-generation advanced process control technology and discover the transformative impact it can have on operations. This technology not only boosts efficiency but also supports sustainable practices within the industry.

Producers like yourself possess the expertise required to maneuver through the intricate hurdles of remaining competitive in today’s market landscape. This is applicable across a wide range of sectors, including pharmaceuticals, consumer goods, food and beverage, mining, and chemicals. Therefore, embracing the latest technological innovations is essential for advancing your ongoing digital transformation efforts. Throughout your organization, from the control room to executive meetings, users of process systems consistently grapple with the challenge of optimizing productivity while managing budget limitations and resource availability, all while tackling shifting operational risks. By addressing these challenges head-on, you can unlock significant productivity enhancements across your facility with the PlantPAx distributed control system (DCS). The features of this system can greatly influence the lifespan of your plant operations, ensuring that integrated and scalable systems enhance productivity, boost profitability, and minimize operational risks. Ultimately, investing in such advanced systems can lead to a more resilient and efficient production environment.

The System 800xA is more than just a Distributed Control System (DCS); it serves as an Electrical Control System, a Safety system, and a platform for collaboration, designed to enhance engineering efficiency, operator performance, and asset utilization. With its integrated electrical control capabilities, the ABB Ability System 800xA allows for comprehensive management of the entire electrical system, spanning from high-voltage switchgear to low-voltage motor control systems. Whether used in conjunction with the 800xA DCS or independently, this system is an optimal choice for managing electrical controls. By utilizing intelligent devices, users can significantly reduce the amount of hardwired cabling required for switchgear, regardless of the standard protocols in place. The digital communication's high reliability not only enhances the flow of information from devices but also allows for the elimination of additional electrical measurement instruments, thereby streamlining operations and improving overall system efficiency. Ultimately, the ABB Ability System 800xA stands out as a versatile and effective solution in modern electrical control management.

Navigating the complexities of industrial processes presents a significant challenge for businesses striving to be both responsive to market demands and economically viable. To meet these challenges, manufacturers are required to refine their production techniques in order to offer an expanded range of higher-value items while also accommodating shorter production runs. It is essential for them to enhance output, optimize operational efficiency, and elevate product quality to the fullest extent permitted by their existing equipment. Achieving this necessitates maximizing equipment uptime and facilitating smoother transitions while minimizing waste. Furthermore, there is an increasing expectation from the public for manufacturers to lessen their environmental footprint and adhere to strict emissions regulations. Rockwell Automation Pavilion8® Model Predictive Control (MPC) technology serves as an advanced intelligence layer that integrates with automation systems, continuously steering the plant toward achieving a multitude of business goals—including cost savings, reduced emissions, consistent quality, and increased production—while operating in real time. This innovative approach not only enhances operational effectiveness but also aligns with sustainability initiatives, positioning manufacturers for success in an evolving marketplace.

The DeltaV S-series Electronic Marshalling utilizing CHARMs allows for flexible placement of field cabling, independent of the type of signal or control methodology employed. The DeltaV™ Distributed Control System (DCS) is designed to streamline automation processes, which reduces operational challenges and mitigates project risks effectively. This advanced collection of products and services enhances plant efficiency through intuitive control solutions that are straightforward to manage and service. Importantly, the DeltaV DCS is adaptable, easily scaling to fit your specific requirements without introducing additional complexity. Furthermore, the seamless integration capabilities of the DeltaV system encompass various functions including batch processing, advanced control, change management, engineering tools, diagnostics, and much more, ensuring a comprehensive solution for your operational needs. Ultimately, this flexibility and integration pave the way for improved productivity and reliability in industrial environments.

Pitops stands out as the sole software solution capable of executing genuine closed-loop system identification with PID controllers in Auto mode, or even with secondary PID controllers in Cascade mode, without the necessity of interrupting the cascade chain or undertaking additional, labor-intensive plant step tests. No other competitor tool is capable of successfully identifying transfer functions using data from PID controllers in Cascade mode, making Pitops unparalleled in this regard. Additionally, Pitops conducts transfer function identification entirely in the time domain, unlike other tools that rely on the more intricate Laplace (S) or Discrete (Z) domains. It also has the remarkable ability to manage multiple inputs and identify several transfer functions simultaneously. Utilizing a groundbreaking proprietary algorithm, Pitops facilitates multiple inputs closed-loop transfer function system identification in the time domain, significantly surpassing traditional methods like ARX/ARMAX/Box and Jenkins used by competing tools. This innovative approach not only streamlines the process but also enhances accuracy and efficiency, making Pitops the preferred choice in the industry.

Modelon’s OPTIMICA Compiler Toolkit stands out as the market's leading Modelica-based mathematical engine, providing users with a robust solution for automating, simulating, and optimizing system behaviors across the model-based design cycle. As the trusted compiler for Modelon Impact, OPTIMICA allows users to construct multi-domain physical systems by selecting from a vast library of model components. The toolkit’s cutting-edge solvers facilitate the evaluation of intricate physical systems, accommodating both transient simulations and steady-state calculations, as well as dynamic optimization. With its advanced mathematical capabilities, OPTIMICA can effectively manipulate and streamline models to enhance performance and reliability, catering to diverse industries and applications that range from automotive and active safety to energy and power generation optimization. Given the growing demand for effective power regulation in the contemporary energy landscape, optimizing the startup processes of thermal power plants has become a critical industrial requirement. Furthermore, the flexibility and efficiency of OPTIMICA make it an invaluable asset for engineers tackling complex system challenges.

Apromon is a web-based software solution designed for assessing the performance of PID loop controls, including both primary and Advanced Process Control (APC) loops. It offers a comprehensive evaluation for individual loops, cascade configurations, APC loops, and even for signals that only have a process variable (PV) without a connected controller. One of Apromon's standout features is its ability to automatically translate various types of controllers—such as those for flow, pressure, temperature, and level—into a unified “grade” factor, similar to the scores teachers assign to students during evaluations. This grading system ranges from 0 to 100, where 100 represents optimal performance and 0 signifies the lowest. Furthermore, Apromon operates continuously, ensuring that performance metrics are consistently calculated and stored without any interruptions, unlike some competing products that may overlook certain tags during monitoring. This relentless dedication to performance tracking guarantees that users have real-time insights into their control systems' efficiency at all times.

CVXOPT is an open-source software library designed for convex optimization, leveraging the capabilities of the Python programming language. Users can interact with it through the Python interpreter, execute scripts from the command line, or incorporate it into other applications as Python extension modules. The primary goal of CVXOPT is to facilitate the development of convex optimization software by utilizing Python's rich standard library and the inherent advantages of Python as a high-level programming tool. It provides efficient Python classes for both dense and sparse matrices, supporting real and complex numbers, along with features like indexing, slicing, and overloaded operations for performing matrix arithmetic. Additionally, CVXOPT includes interfaces to various solvers, such as the linear programming solver in GLPK, the semidefinite programming solver in DSDP5, and solvers for linear, quadratic, and second-order cone programming available in MOSEK, making it a versatile tool for researchers and developers in the field of optimization. This comprehensive set of features enhances its utility in tackling a wide range of optimization problems.

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

OpenModelica serves as an open-source platform for modeling and simulating systems using the Modelica language, catering to both industrial and academic sectors. Its progress is driven by the Open Source Modelica Consortium (OSMC), a non-profit entity. This platform seeks to deliver an extensive environment for Modelica modeling, compilation, and simulation, available in both binary and source code formats, thereby supporting research, education, and practical applications in the industry. OpenModelica is compatible with multiple operating systems, such as Windows, Linux, and macOS, and fully supports the Modelica Standard Library. It is crafted to enable the creation and execution of a wide range of numerical algorithms, making it ideal for tasks like control system design, nonlinear equation resolution, and the development of optimization algorithms for intricate applications. Additionally, the platform incorporates features for debugging, visualization, and animation, which not only enhance user interaction but also streamline the modeling and simulation processes significantly. Overall, OpenModelica’s versatility and robust tools make it a valuable asset for engineers and researchers alike.

Robyn is a cutting-edge, open-source Marketing Mix Modeling (MMM) tool created by Meta’s Marketing Science team for experimental purposes. It aims to assist advertisers and analysts in constructing thorough, data-driven models that assess how various marketing channels affect business results, such as sales and conversions, while ensuring privacy through aggregated data. Instead of depending on tracking individual users, Robyn delves into historical time-series data by integrating marketing expenditure or reach information—encompassing ads, promotions, and organic initiatives—with performance indicators to evaluate incremental impacts, saturation effects, and carry-over dynamics. The package utilizes a combination of classical statistical techniques and contemporary machine learning methods; it employs ridge regression to mitigate multicollinearity in complex models, performs time-series decomposition to differentiate between trends and seasonal patterns, and incorporates a multi-objective evolutionary algorithm for optimization. This innovative approach allows businesses to gain deeper insights into their marketing effectiveness and make more informed decisions based on robust analysis.

VirtualLab Fusion is a cutting-edge optical design software that streamlines fast physical optics modeling by linking different field solvers via a distinctive operator and channel approach. This integration allows for effective simulations that achieve a harmonious balance between precision and speed. The software comes equipped with an array of packages customized for particular optical design requirements, offering an assortment of tools and features to cater to various applications. With its user-friendly interface, VirtualLab Fusion makes the design process more accessible, enabling users to prioritize innovation and optimization in their projects. Additionally, the platform includes resources such as tips, tricks, training sessions, and webinars to further boost user expertise and proficiency in utilizing the software. This comprehensive support ensures that users can fully leverage the capabilities of the software for their optical design endeavors.

Statsmodels is a Python library designed for the estimation of various statistical models, enabling users to perform statistical tests and explore data effectively. Each estimator comes with a comprehensive array of result statistics, which are validated against established statistical software to ensure accuracy. This package is distributed under the open-source Modified BSD (3-clause) license, promoting free use and modification. Users can specify models using R-style formulas or utilize pandas DataFrames for convenience. To discover available results, you can check dir(results), and you will find that attributes are detailed in results.__doc__, while methods include their own docstrings for further guidance. Additionally, numpy arrays can be employed as an alternative to formulas. For most users, the simplest way to install statsmodels is through the Anaconda distribution, which caters to data analysis and scientific computing across various platforms. Overall, statsmodels serves as a powerful tool for statisticians and data analysts alike.

Enhance artificial intelligence by utilizing a diverse array of models. AI models serve as innovative building blocks, and Sieve provides the simplest means to leverage these components for audio analysis, video generation, and various other applications at scale. With just a few lines of code, you can access cutting-edge models and a selection of ready-to-use applications tailored for numerous scenarios. You can seamlessly import your preferred models similar to Python packages while visualizing outcomes through automatically generated interfaces designed for your entire team. Deploying custom code is a breeze, as you can define your computational environment in code and execute it with a single command. Experience rapid, scalable infrastructure without the typical complexities, as Sieve is engineered to automatically adapt to increased traffic without any additional setup required. Wrap models using a straightforward Python decorator for instant deployment, and benefit from a comprehensive observability stack that grants you complete insight into the inner workings of your applications. You only pay for what you consume, down to the second, allowing you to maintain full control over your expenditures. Moreover, Sieve's user-friendly approach ensures that even those new to AI can navigate and utilize its features effectively.

Tellurium is a versatile Python package that integrates several key tools necessary for conducting simulation studies in systems biology and various other fields. It offers access to the robust high-performance simulation engine known as libRoadrunner. Users can create their models using Antimony, a simplified, human-readable format based on SBML. Additionally, an Antimony tutorial is available for those looking to learn the specifics of the language. Tellurium is compatible with all major standards, including SBML, SED-ML, and COMBINE archives. It can be utilized through user-friendly GUI environments like Spyder, PyCharm, or Jupyter Notebooks, including CoLab, which enhances productivity and supports interactive editing features. Installation is straightforward, achieved through the standard pip method, and there is also a convenient one-click installer available for Windows users that sets up a comprehensive environment for systems biology modeling. The development of Tellurium benefits from the contributions of a wide range of open-source developers, ensuring a continually evolving tool for researchers. Overall, Tellurium stands out as an essential resource for those involved in simulation studies across multiple scientific disciplines.

Ansys Icepak serves as a computational fluid dynamics (CFD) solver specifically designed for managing thermal issues in electronic devices. It offers insights into airflow, temperature distributions, and heat transfer phenomena within integrated circuit packages, printed circuit boards (PCBs), electronic assemblies, and power electronics. By leveraging the top-tier Ansys Fluent CFD solver, Ansys Icepak delivers robust cooling solutions tailored for electronic components, allowing for thorough thermal and fluid flow evaluations. The software operates through the Ansys Electronics Desktop (AEDT) graphical user interface (GUI), facilitating comprehensive analyses of heat transfer involving conduction, convection, and radiation. Moreover, it boasts sophisticated features for modeling both laminar and turbulent flow conditions, as well as conducting species analysis that incorporates radiation and convection effects. Ansys’ extensive PCB design platform empowers users to perform simulations on PCBs, ICs, and packages, enabling a precise assessment of complete electronic systems, thereby enhancing design efficiency and performance optimization. Thus, Ansys Icepak stands out as an essential tool for engineers aiming to improve thermal management in their electronic designs.

WhichModel provides a comprehensive AI benchmarking platform that enables users to compare, test, and optimize dozens of AI models to find the ideal fit for their application needs. By supporting over 50 AI models, including leading providers like OpenAI, Anthropic, and Google, the platform allows side-by-side comparisons using the same inputs and custom parameters. Its prompt optimization features help users discover the most effective prompts across different models, improving AI performance. Continuous evaluation tools let users track performance trends over time, ensuring they stay updated with model changes and improvements. The platform addresses common AI challenges such as model selection paralysis, inconsistent performance, hidden costs, and time-consuming testing processes. WhichModel offers flexible pay-as-you-go credit packages, eliminating subscription waste and letting users pay only for benchmarks they run. With real-time testing capabilities and detailed analytics on accuracy, speed, and cost-efficiency, users can confidently choose the best AI for their projects. Responsive 24/7 customer support adds an extra layer of assistance for users of all experience levels.

Turing enhances and simplifies the development of consumer packaged goods (CPG) products with intelligent guidance. It eliminates the need for manual and physical analysis, allowing teams to move swiftly from ideation to production all within a single platform. Many organizations waste millions of dollars on the endless collection and integration of data to create precise models. However, Turing's advanced algorithms need far less data compared to standard machine learning or artificial intelligence solutions. This means no more prolonged periods spent on data aggregation. Turing equips research and development teams with the tools to merge typically incompatible data from diverse systems into one cohesive platform. Utilize Turing’s AI to uncover valuable insights and combinations that were once hidden within isolated data silos. This leads to the creation of superior prototypes at an accelerated pace. With Turing, collaboration and co-creation are seamless across all phases of product development, from initial concept to market launch, all within a single integrated platform. Make informed, data-driven choices regarding products, packaging, or processes in real-time, avoiding the drawn-out and inefficient cycle of physical trial and error. This innovative approach to product development not only saves time but also enhances overall efficiency, driving quicker market entries and better outcomes.

Piper is a rapidly operating, localized neural text-to-speech (TTS) system that is particularly optimized for devices like the Raspberry Pi 4, aiming to provide top-notch speech synthesis capabilities without the dependence on cloud infrastructure. It employs neural network models developed with VITS and subsequently exported to ONNX Runtime, which facilitates both efficient and natural-sounding speech production. Supporting a diverse array of languages, Piper includes English (both US and UK dialects), Spanish (from Spain and Mexico), French, German, and many others, with downloadable voice options available. Users have the flexibility to operate Piper through command-line interfaces or integrate it seamlessly into Python applications via the piper-tts package. The system boasts features such as real-time audio streaming, JSON input for batch processing, and compatibility with multi-speaker models, enhancing its versatility. Additionally, Piper makes use of espeak-ng for phoneme generation, transforming text into phonemes before generating speech. It has found applications in various projects, including Home Assistant, Rhasspy 3, and NVDA, among others, illustrating its adaptability across different platforms and use cases. With its emphasis on local processing, Piper appeals to users looking for privacy and efficiency in their speech synthesis solutions.

This package is designed for independent reuse across any Python project and is maintained by the Zope Toolkit initiative. It serves as an implementation of "object interfaces" within the Python ecosystem. Interfaces act as a way to designate objects as adhering to a specific API or contract, making this package a practical example of applying the Design By Contract methodology in Python. Essentially, interfaces are objects that detail (document) the expected external behavior of the objects that implement them. An interface articulates behavior through a combination of informal documentation within a docstring, attribute definitions, and invariants, which are the necessary conditions that must be satisfied by the objects implementing the interface. Attribute definitions specify particular attributes, outlining their names while offering documentation and constraints regarding the allowed values for those attributes. These definitions can take various forms, allowing for flexibility in how they are expressed. Furthermore, the ability to define interfaces enhances the clarity and reliability of code by ensuring that objects conform to specified behaviors.

Radiance is designed to assist lighting designers and architects in forecasting the illumination levels and visual characteristics of a space before it is built. This software package comprises tools for modeling and converting scene geometry, luminaire specifications, and material attributes, all essential for the simulation process. Utilizing ray tracing techniques, the lighting simulation calculates radiance values—representing the amount of light that travels through a specific point in a designated direction—and typically presents these values in a high-quality photographic image format. Users can analyze, view, and modify the resulting images within the software, as well as export them to various widely-used image file formats, thus enabling the creation of printed outputs. Of particular importance to users is the primary component of the Radiance package: the lighting simulation engine, which not only determines light levels but also generates detailed images, allowing for enhanced visualization and assessment of lighting designs. Furthermore, this capability aids in refining the design process by offering insights into potential adjustments before actual construction begins.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

MindSpore, an open-source deep learning framework created by Huawei, is engineered to simplify the development process, ensure efficient execution, and enable deployment across various environments such as cloud, edge, and device. The framework accommodates different programming styles, including object-oriented and functional programming, which empowers users to construct AI networks using standard Python syntax. MindSpore delivers a cohesive programming experience by integrating both dynamic and static graphs, thereby improving compatibility and overall performance. It is finely tuned for a range of hardware platforms, including CPUs, GPUs, and NPUs, and exhibits exceptional compatibility with Huawei's Ascend AI processors. The architecture of MindSpore is organized into four distinct layers: the model layer, MindExpression (ME) dedicated to AI model development, MindCompiler for optimization tasks, and the runtime layer that facilitates collaboration between devices, edge, and cloud environments. Furthermore, MindSpore is bolstered by a diverse ecosystem of specialized toolkits and extension packages, including offerings like MindSpore NLP, making it a versatile choice for developers looking to leverage its capabilities in various AI applications. Its comprehensive features and robust architecture make MindSpore a compelling option for those engaged in cutting-edge machine learning projects.

Guidewheel's AI platform will help you get more out of existing assets, reduce costs, and make your team win. The fastest way to improve factory operation. Track downtime accurately and identify root causes for improved efficiency and utilization. Forecast throughput accurately and track planned production vs. actual. Monitor OEE in real-time and see your trend over time. Track cycles, cycle times, and performance against goals. Monitor energy consumption and look for ways to reduce costs and consumption. Find out about maintenance issues before they become a problem. Monitor conditions such as temperature, flow, humidty, and pressure. Unlock hidden capacity by revealing and addressing losses such as preventable downtimes, long changeovers and late starts. AI-driven alerts will notify you when performance is deviating from the plan. This allows your team to take corrective actions and stay on track. Delivering on time is the best way to build customer trust.

Accelerate your AI deployment with fully managed, cloud-based AI solutions that come with comprehensive support for GPT-4, eliminating the need for API tokens. Utilize our low-code framework to streamline your development process, as built-in integrations with all major AI models simplify your workflow. Instantly deploy an API and enjoy the ability to scale and share your applications without the burden of infrastructure management. Transform a smart prompt into a sharable published API while incorporating logic and routing capabilities using Python. Steamship seamlessly connects with your preferred models and services, allowing you to avoid the hassle of learning different APIs for each provider. The platform standardizes model output for consistency and makes it easy to consolidate tasks such as training, inference, vector search, and endpoint hosting. You can import, transcribe, or generate text while taking advantage of multiple models simultaneously, querying the results effortlessly with ShipQL. Each full-stack, cloud-hosted AI application you create not only provides an API but also includes a dedicated space for your private data, enhancing your project's efficiency and security. With an intuitive interface and powerful features, you can focus on innovation rather than technical complexities.

Aera's Cognitive Operating System™ serves as the digital intelligence for your organization, enhancing the speed, scalability, and precision of your decision-making capabilities. This is accomplished by effectively managing the four essential architectural pillars necessary for Cognitive Automation: data, science, process, and change. Utilizing Aera's innovative real-time crawling technology, it gathers, harmonizes, and enriches billions of transactions from intricate enterprise systems and external data sources while maintaining their context. Consequently, all internal and external data crucial for decision automation and enhancement is made available instantly. Aera integrates advanced methodologies such as optimization, predictions, modeling, and artificial intelligence (AI) directly into the decision-making framework. Additionally, domain knowledge is embedded within proprietary models that feature thousands of pre-defined metrics applicable across various business sectors. You can either use your own algorithms, create custom ones, or take advantage of our ready-to-use solutions, allowing for a flexible approach to meet your organization's unique needs. This versatility ensures that your decision-making processes are not only efficient but also tailored to the specific dynamics of your industry.

ExtendSim offers a comprehensive suite of tools suitable for various simulation tasks. Regardless of your intended application—whether it's model creation, execution, or analysis—there's a tailored package available to meet your specific requirements. The product range includes options for model construction, experimentation, and analytical assessment, along with Model Developer Editions and specialized packages focused on simulation execution, model evaluation, or educational purposes. To determine the most appropriate package for your needs, you can explore the User Types section. Users can create, utilize, and dynamically run models, develop custom interfaces and components, as well as exchange and control data with other software applications. Each Model Developer Edition (MDE) of ExtendSim retains a unified core of functionalities, allowing users to design and assemble models and interfaces, adjust parameters, execute simulations and animations, conduct experiments, and perform analyses and optimizations—all while being able to save and export their results effectively. This flexibility ensures that users can fully leverage ExtendSim's capabilities to enhance their simulation experience.

Train in Data is a specialized online school offering intermediate to advanced courses in Python programming, data science, and machine learning designed for serious learners. Led by industry professionals with extensive experience developing and deploying machine learning models in production environments, the platform provides practical, hands-on instruction. Its teaching philosophy centers on mastering the theory behind machine learning before applying it through Python coding exercises. The courses help students build a strong, intuitive understanding of complex concepts while reinforcing learning through real coding practice. Train in Data delivers a comprehensive learning experience by combining theoretical knowledge, practical coding skills, and troubleshooting techniques. This prepares learners to confidently tackle real-world machine learning projects from start to finish. The platform is ideal for those looking to deepen their expertise and transition from theory to practice. Ultimately, it empowers students to become proficient machine learning practitioners.

Conda serves as an open-source solution for managing packages, dependencies, and environments across various programming languages, including Python, R, Ruby, Lua, Scala, Java, JavaScript, C/C++, Fortran, and others. This versatile system operates seamlessly on multiple platforms such as Windows, macOS, Linux, and z/OS. With the ability to swiftly install, execute, and upgrade packages alongside their dependencies, Conda enhances productivity. It simplifies the process of creating, saving, loading, and switching between different environments on your device. Originally designed for Python applications, Conda's capabilities extend to packaging and distributing software for any programming language. Acting as an efficient package manager, it aids users in locating and installing the packages they require. If you find yourself needing a package that depends on an alternate Python version, there’s no need to switch to a different environment manager; Conda fulfills that role as well. You can effortlessly establish an entirely separate environment to accommodate that specific version of Python, while still utilizing your standard version in your default environment. This flexibility makes Conda an invaluable tool for developers working with diverse software requirements.

The Synopsys 3DIC Compiler, which is founded on the premier digital implementation platform in the industry and leverages a unified fusion data model, facilitates a smooth transition to 2.5/3D heterogeneous integration. This comprehensive platform supports analysis-driven feasibility studies, multi-die partitioning, and the selection of foundry technology for both prototyping and floor planning in a single environment. As a result, it promotes effective design implementation that encompasses advanced packaging and die-to-die routing, coupled with golden signoff verification. Additionally, the 3DIC Compiler collaborates with Synopsys 3DSO.ai, the first autonomous AI optimization solution in the market for multi-die designs, enhancing system performance and ensuring quality outcomes regarding thermal integrity, signal integrity, and power network design. Widely embraced by clients, the 3DIC Compiler platform has also received certification from prominent foundries, validating its capabilities in advanced process and packaging technologies. This combination of features positions the 3DIC Compiler as a vital tool for engineers aiming to innovate in the realm of integrated circuit design.

The ProShop Management System Package consists of 14 distinct modules aimed at equipping your print shop with essential information. This comprehensive package encompasses all features found in the Junior package, in addition to modules for inventory management, scheduling, job costing, and beyond! The core ProShop Management System includes four key modules that enable you to effectively schedule, manage both raw and finished goods inventory, and monitor job statuses. Easily track the progress of each job through your shop and assess costing factors to ensure profitability on every project. With ProShop Scheduling, you gain a clear overview of all jobs from initiation to completion. You can seamlessly adjust and rearrange the schedule as required using the on-screen progress table or simply by clicking and dragging scheduled tasks. The system allows for effortless updates and modifications to estimated timeframes. Additionally, you can opt for the estimating function to organize the job schedule automatically or choose to schedule each job on a case-by-case basis. Overall, ProShop Scheduling provides you with a streamlined workflow management tool, enabling you to plan your workdays efficiently with just a few clicks. This innovative approach not only boosts productivity but also enhances overall operational efficiency in your print shop.

The websockets library offers a comprehensive implementation of the WebSocket Protocol (RFC 6455 & 7692) for creating both WebSocket servers and clients in Python, emphasizing accuracy, simplicity, durability, and high performance. Utilizing asyncio, which is Python’s built-in asynchronous I/O framework, it presents a sophisticated coroutine-based API that streamlines development. The library has undergone extensive testing to ensure it meets the requirements outlined in RFC 6455, and its continuous integration process mandates that every branch achieves 100% coverage. Designed specifically for production environments, websockets was notably the first library to effectively address backpressure issues before they gained widespread attention in the Python ecosystem. Furthermore, it offers optimized and adjustable memory usage, and utilizes a C extension to enhance performance for demanding operations. The library is conveniently pre-compiled for Linux, macOS, and Windows, and is distributed in wheel format tailored for each system and Python version. With websockets managing the intricate details, developers can dedicate their efforts to building robust applications without concern for the underlying complexities. This makes it an essential tool for developers looking to harness the full potential of WebSocket technology.

GAMS, which stands for General Algebraic Modeling System, is a premier software for mathematical modeling praised for its exceptional performance, scalability, and user-friendly interface. With the recent launch of GAMSPy, users can now seamlessly merge GAMS functionalities with Python, thus enhancing the efficiency and versatility of model development within the Python environment. The platform's algebraic modeling language greatly simplifies the formulation of optimization challenges, leading to optimal outcomes through the use of advanced mathematical solvers. Furthermore, GAMS MIRO introduces intuitive graphical interfaces for managing GAMS models, supporting both local and cloud-based deployment alongside sophisticated visualization tools. For those seeking scalable solutions, the GAMS Engine provides a dependable software as a service (SaaS) option, making it possible to execute models either on local servers or in the cloud. In addition to these features, GAMS is committed to supporting its users through various workshops, training sessions, and consulting services, aimed at enhancing their ability to create, refine, and implement effective decision-support systems. This comprehensive approach ensures that users are well-equipped to leverage GAMS to its fullest potential, fostering innovation and efficiency in their modeling endeavors.

NetworkX is a Python library designed for constructing, altering, and analyzing the intricacies, behaviors, and functionalities of complex networks. It offers generators for various types of graphs, including traditional, random, and synthetic networks. The advantages of using Python further enhance the experience, providing quick prototyping capabilities, ease of learning, and compatibility across multiple platforms. Additionally, it facilitates a comprehensive examination of network structures and the application of various analytical measures. This makes NetworkX an invaluable tool for researchers and practitioners in the field of network science.

DemandPlanner facilitates the creation of precise sales and demand projections for your products and retail locations, taking into account customer and market fluctuations. As a category planner, you benefit from enhanced visibility, allowing you to devise optimal replenishment and inventory strategies. The tool provides reliable sales forecasts by evaluating the effects of numerous external factors that impact product demand. Utilizing sophisticated quantitative analytics methods, such as Bayesian regression, machine learning, and time series analysis, DemandPlanner generates well-informed estimates for both short-term and long-term periods. Its collaborative functionalities simplify the demand planning process while ensuring that replenishment, procurement, and inventory management are effectively optimized. By leveraging advanced predictive analytics models, you can accurately assess and comprehend customer demand trends for your products. Additionally, it enables you to effectively manage the net working capital invested in inventory through meticulous, data-driven strategies that enhance overall operational efficiency. Ultimately, DemandPlanner empowers businesses to adapt swiftly to changing market conditions while maintaining alignment with customer preferences.

Our cutting-edge Marketing Analytics and optimization software delivers a cohesive platform for sophisticated marketing investment analysis. Within the data visualization component, users—regardless of their analytic expertise—can explore and interpret various visual representations and statistics essential for validating data as well as generating preliminary insights and hypotheses regarding demand drivers. The fundamental Modeling module features an extensive user interface (UI) that allows for the configuration of diverse dynamic linear panel models across all tiers of the client’s business operations. Moreover, user-defined model architectures seamlessly integrate with the model database, enabling necessary variable transformations that are then conveyed to the proprietary model estimation algorithm contained within the OxMetrics analytics suite. This integration ensures that users can efficiently analyze their marketing strategies and make data-driven decisions.

treeDiM is a software company specializing in CAD and PLM solutions tailored for packaging design as well as point-of-sale displays. The Picador CAD software is actively utilized by more than 4,500 users across 55 countries, including packaging manufacturers, digital printing firms, industrial enterprises, brand owners, and designers. Meanwhile, the collaborative platform PLMPack has attracted over 15,000 users globally. One of our groundbreaking initiatives, the collaborative ecodesign platform PLM Pack, boasts various software modules, including PackLIB, a collection of parametric packaging model libraries, and StackBuilder, which optimizes palletizing, case packing, and truck loading. Additionally, our PackStress module provides essential calculations for packing and stacking durability. Recently, Optimus introduced a new Packaging Estimating module, featuring automated calculations for printing imposition, die-cutting, case packing, and palletization, enabling packaging manufacturers to effectively meet the growing demands of their clients while significantly shortening their time-to-market. This suite of tools not only enhances efficiency but also promotes sustainable practices within the industry.