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Nvidia has open-sourced its Modulus platform, a hardware and software solution combining machine learning and physics-based simulation to create more accurate and efficient digital twins.

A digital twin refers to a computer-based model or simulation that imitates the behavior and characteristics of a physical object or process. They are created by collecting data from various sensors, instruments, and other sources and then feeding that data into a digital model that simulates the behavior and characteristics of the physical object or system in real time. The use of digital twins is becoming increasingly popular as they provide a way to test and optimize systems in a virtual environment before deploying them in the real world. This can save time and money and also improve safety and performance.

Modulus is a machine learning platform that combines physics-based simulation to create more accurate digital twins. The purpose of this platform is to encourage collaboration, transparency, and accountability in machine learning. The platform is licensed as open-source software under the Apache 2.0 license, and the complete source code is available in the GitHub repository. This move makes it easier for researchers to access and utilize the Modulus platform, which has the potential to revolutionize various industries by improving the accuracy of digital twins.

According to Nvidia, open-sourcing Modulus has several benefits:

It makes it easier for users to collaborate and share their work results with wider communities.

Disclosing the code and data enhances the transparency and repeatability of physical machine learning. Multiple scientists can verify and reproduce the results, leading to more reliable research outcomes.

Open-source workflows promote innovation by allowing more people to build on the work of their predecessors.

Open-sourcing Modulus makes research more accessible to stakeholders, expanding the impact of physics-based modeling research.
Nvidia has made Modulus accessible to users in various fields, who can use, develop, and contribute to its projects. The Modulus team has collaborated with enterprises and AI researchers over the past few years to expand the platform’s capabilities and cover more fields. They have also added physics-driven methods to solve industrial-scale problems. As a result, Modulus now includes various data-driven neural operation sub-architectures, such as graph neural networks and physical information neural networks. For instance, meteorological researchers can use FourCastNet on Modulus to dynamically simulate physical machine-learning models of the global climate.

Although the reference samples are a helpful starting point for engineers and developers, much work still needs to be done. This includes conducting fundamental research on generalizable models, as well as applying these models to various real-world applications. Such work requires a community-driven effort to leverage these technologies’ potential fully.

Check out the NVIDIA/modulus repository and Reference Article. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 16k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.
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29/03/2023 – 10:42 /Niharika Singh
Twitter: @hoffeldtcom