Introduction to Physics-Informed Machine Learning With NVIDIA Modulus

Introduction to Physics-Informed Machine Learning With NVIDIA Modulus

NVIDIA NIC-NVI-IPIM

USD 30.00
excl. VAT

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

The minimum order quantity for NVIDIA self-paced courses is 10.
When adding a course to the shopping cart, a quantity of 10 is automatically added. It may also take 2-3 working days for your course access to be activated. You will receive an email from us with all the necessary information.
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