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features
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support

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Description

Utilizing sophisticated analytics and machine learning techniques is essential for minimizing operational expenses and mitigating risks. A fundamental component of the digital transformation landscape, digital twins provide precise virtual representations of tangible assets, systems, and objects to enhance productivity, optimize processes, and drive profitability. Typically, a digital twin is regarded as a software model of a physical asset or system that is tailored to identify, avert, predict, and refine processes through real-time analytics, ultimately delivering significant business advantages. At GE Digital, our emphasis lies in leveraging digital twin software to assist our clients in three primary domains: Asset, Network, and Process. By effectively monitoring, simulating, and managing an asset, process, or network, organizations can significantly elevate system performance. Furthermore, it is crucial to ensure the well-being and safety of employees and the environment while achieving business goals by minimizing incidents related to assets and processes, as well as preventing unintended downtimes, thereby fostering a more resilient operational framework. The integration of digital twin technology not only enhances efficiency but also paves the way for innovation across various sectors.

Description

NVIDIA Modulus is an advanced neural network framework that integrates the principles of physics, represented through governing partial differential equations (PDEs), with data to create accurate, parameterized surrogate models that operate with near-instantaneous latency. This framework is ideal for those venturing into AI-enhanced physics challenges or for those crafting digital twin models to navigate intricate non-linear, multi-physics systems, offering robust support throughout the process. It provides essential components for constructing physics-based machine learning surrogate models that effectively merge physics principles with data insights. Its versatility ensures applicability across various fields, including engineering simulations and life sciences, while accommodating both forward simulations and inverse/data assimilation tasks. Furthermore, NVIDIA Modulus enables parameterized representations of systems that can tackle multiple scenarios in real time, allowing users to train offline once and subsequently perform real-time inference repeatedly. As such, it empowers researchers and engineers to explore innovative solutions across a spectrum of complex problems with unprecedented efficiency.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

APERIO DataWise Yes 

Integrations

APERIO DataWise No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

GE Digital

Founded

2015

Country

United States

Website

www.ge.com/digital/applications/digital-twin

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/modulus

Product Features

Simulation

1D Simulation No 
3D Modeling No 
3D Simulation No 
Agent-Based Modeling No 
Continuous Modeling No 
Design Analysis No 
Direct Manipulation No 
Discrete Event Modeling No 
Dynamic Modeling No 
Graphical Modeling No 
Industry Specific Database No 
Monte Carlo Simulation No 
Motion Modeling No 
Presentation Tools No 
Stochastic Modeling No 
Turbulence Modeling No 

Alternatives

Alternatives

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