High-fidelity simulations in science and engineering are computationally expensive and time-prohibitive for quick iterative use cases, from design analysis to optimization. NVIDIA Modulus, the physics machine learning platform, turbocharges such use cases by building physics-based deep learning models that are 100,000x faster than traditional methods and offer high-fidelity simulation results.
Upon completion, you will have an understanding of the various building blocks of Modulus and the basics of physics-informed deep learning. You’ll also have an understanding of how the modulus framework integrates with the overall Omniverse Platform.
Course Prerequisites
- Familiarity with the Python programming language
- An understanding of partial differential equations and their use in physics.
- Familiarity with machine learning concepts like training and inference.
What you will learn
Upon completion, you’ll understand the various building blocks of Modulus and the basics of physics-informed deep learning. You’ll also understand how the Modulus framework integrates with the overall Omniverse platform.
Additional information
PLEASE NOTE: It may take 2-3 business days for your course access to be activated. You will receive an email from us with all necessary details.
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