Introduction to Physics-Informed Machine Learning With NVIDIA Modulus

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.

• 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.

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.