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Teratec 2022 Forum
Wednesday, June 15 - Technical Workshop

Workshop 08 - 2:00 to 5:30 pm

High performance AI in the industry
Chaired by Cristel Saudemont, France Director, Supercomputing & AI , Higher Education and Research and Frédéric Parienté, senior manager in the solutions architecture and engineering group, Nvidia

Physics-Informed Neural Networks with NVIDIA Modulus: Application to external flow problems
Par Niki Loppi, AI/HPC solutions architect, Nvidia

In this talk, we will review the fundamentals of Physics-informed Neural Networks (PINN) and explore the capabilities of NVIDIA Modulus framework for solving external flow problems that are common in various aero/hydrodynamic applications. Specifically, we will focus on how Modulus is able to train parameterised PINNs which, after training, can be used simulate different flow conditions almost instantaneously. Furthermore, we will also present how users can undertake unsteady (time-dependent) simulations using the moving time-window approach. Finally, we will show results for a parameterized unsteady case which demonstrates that PINNs are able to capture temporal dynamics across a range of Reynolds numbers.

Biographie  : Niki is an AI/HPC solutions architect at NVIDIA, helping academic researchers to leverage NVIDIA’s technology stack through the NVIDIA AI Technology Center program. Prior to joining NVIDIA, he worked as a researcher in the Department of Aeronautics at Imperial College London, where he also obtained his PhD in Computational Fluid Dynamics. Specifically, his research focused on the development of high-order accurate numerical methods for solving large-scale incompressible fluid flow problems using massively parallel modern GPU architectures.

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