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Date: February 10, 2021
Time: 10:00am – 11:00am PT
Duration: 1 hour

Simulations are pervasive in science and engineering fields and have been recently advanced by physics-driven AI. To enable hands-on learning and research breakthroughs, it's critical for universities to equip these disciplines with accelerated simulation tools.

Join this webinar to learn how NVIDIA SimNet addresses a wide range of use cases involving coupled forward simulations without any training data, as well as inverse and data assimilation problems. Researchers can customize it with APIs to implement new geometry and physics. It also has advanced network architectures that are optimized for high-performance GPU computing and offers scalable performance for multi-GPU and multi-node implementations with accelerated linear algebra.

By attending this webinar you'll learn:
  • Neural network solver methodology and the SimNet architecture
  • Real-world use cases, ranging from challenging forward multi-physics simulations with turbulence and complex 3D geometries to industrial design optimization and inverse problems.
  • User implementation of two-phase flow in a porous media in SimNet.
Join us after the presentation for a live Q&A session.



You will receive an email with instructions on how to join the webinar shortly.

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Sanjay Choudhry

Sr. Director, NVIDIA

Sanjay Choudhry is a Senior Director at Nvidia with a background in both traditional computational methods as well as machine learning in science and engineering. He leads the engineering efforts on SimNet and is passionate about development of AI based simulation solutions for industrial applications

Oliver Hennigh

Sr. Engineer, NVIDIA

Oliver Hennigh is a Senior Software Engineer at Nvidia with a background in machine learning and scientific computing. Oliver's interests are in accelerating traditional simulation workflows using machine learning and is the lead developer of SimNet, an AI-Accelerated Multi-Physics Simulation Toolkit.

Cedric G. Fraces

Dept. of Energy Resources Engineering, Stanford University

Cedric holds a master's degree in Energy Resources Engineering from Stanford University and is currently a PhD candidate in Energy Resources Engineering at Stanford. His research entails the application of Physics informed deep learning to reservoir simulation. He is a reservoir engineer with over 14 years of experience in the energy industry working on major oilfields in the US, Canada, China, Iraq, Kuwait, Kazakhstan, Brazil, Mexico and Colombia and been involved in executive decisions concerning the development and management of corresponding assets.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018