High-fidelity simulations in science and engineering are computationally expensive and time-prohibitive for quick iterative use cases, from design analysis to optimization. NVIDIA PhysicsNeMo, the physics machine learning platform, turbocharges such use cases by building physics-based deep learning models that are 100,000 times faster than traditional methods and offer high-fidelity simulation results. In this hands-on workshop, you'll learn the basics of physics-informed deep learning and data-driven deep learning applied to problems having roots in physics, and understand the unique techniques that PhysicsNeMo brings that help you apply deep learning to modeling multi-physics simulations systems.
Prerequisite(s):
Basic familiarity with Computational methods and Partial Differential Equations (PDEs).
Basic familiarity with Python.
Basic familiarity Neural Networks Neural networks.
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