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What is iTwin?

Integrating the necessary data for digital twins can be quite challenging, particularly when faced with differing formats and a variety of sources from multiple vendors. The processes of creating, visualizing, and analyzing digital twins should ideally be straightforward. As engineers committed to innovative solutions, we have designed software that tackles some of the toughest challenges in the infrastructure industry. By integrating engineering data alongside real-time and IoT information, we are able to produce a captivating 3D or 4D immersive experience. This methodology fosters a deeper understanding of infrastructure assets while unlocking the potential inherent in digital data, which can lead to significant advancements. Our solutions arm organizations with the digital tools required to thrive in today’s competitive landscape. Digital twins serve as dynamic digital representations of physical assets, processes, or systems, allowing for a thorough analysis and modeling of their performance. Unlike traditional static 3D representations, digital twins continuously update with data from diverse sources, significantly boosting their accuracy and effectiveness. Adopting this technology not only enhances operational efficiency but also paves the way for groundbreaking innovations. Ultimately, embracing digital twins can transform how organizations perceive and manage their assets.

What is NVIDIA Modulus?

NVIDIA Modulus is a sophisticated neural network framework designed to seamlessly combine the principles of physics, encapsulated through governing partial differential equations (PDEs), with data to develop accurate, parameterized surrogate models that deliver near-instantaneous responses. This framework is particularly suited for individuals tackling AI-driven physics challenges or those creating digital twin models to manage complex non-linear, multi-physics systems, ensuring comprehensive assistance throughout their endeavors. It offers vital elements for developing physics-oriented machine learning surrogate models that adeptly integrate physical laws with empirical data insights. Its adaptability makes it relevant across numerous domains, such as engineering simulations and life sciences, while supporting both forward simulations and inverse/data assimilation tasks. Moreover, NVIDIA Modulus facilitates parameterized representations of systems capable of addressing various scenarios in real time, allowing users to conduct offline training once and then execute real-time inference multiple times. By doing so, it empowers both researchers and engineers to discover innovative solutions across a wide range of intricate problems with remarkable efficiency, ultimately pushing the boundaries of what's achievable in their respective fields. As a result, this framework stands as a transformative tool for advancing the integration of AI in the understanding and simulation of physical phenomena.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Bentley Systems

Date Founded

1984

Company Location

United States

Company Website

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

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/modulus

Categories and Features

Digital Twin

Not specified

Simulation

Not specified

Categories and Features

AI Inference

Not specified

Digital Twin

Not specified

HPC

Not specified

Neural Network

Not specified

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