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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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.

Description

Navigating the complexities of integrating data necessary for digital twins can be difficult, especially when dealing with incompatible formats and diverse sources from various vendors. Creating, visualizing, and analyzing digital twins should not be overly complicated. As fellow engineers dedicated to problem-solving, we have developed software that addresses some of the most intricate engineering issues within the infrastructure sector. By merging engineering data, real-time information, and IoT data, we can craft an engaging 3D or 4D immersive experience. This approach leads to a more profound comprehension of infrastructure assets and the ability to unlock the potential of digital data, ultimately making a significant impact. Our solutions provide the digital capabilities essential for achieving business success. Digital twins represent a dynamic digital version of physical assets, processes, or systems, enabling us to analyze and model their performance comprehensively. Unlike static 3D models, digital twins are perpetually refreshed with data from a variety of sources, enhancing their utility and accuracy. Embracing this technology empowers organizations to optimize operations and drive innovation.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/modulus

Vendor Details

Company Name

Bentley Systems

Founded

1984

Country

United States

Website

www.bentley.com/en/products/product-line/digital-twins/itwin

Product Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

Alternatives

LiveLink for MATLAB Reviews

LiveLink for MATLAB

Comsol Group

Alternatives

GE Digital Twin Reviews

GE Digital Twin

GE Digital
COMSOL Multiphysics Reviews

COMSOL Multiphysics

Comsol Group