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DTSTART:19721003T020000
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BEGIN:VEVENT
DTSTAMP:20260114T163704Z
LOCATION:Meeting Room C4.7\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T160000
DTEND;TZID=Australia/Melbourne:20231212T180000
UID:siggraphasia_SIGGRAPH Asia 2023_sess103_crs_111@linklings.com
SUMMARY:Use of Physics Based AI for Simulation and Modeling in Era of Digi
 tal Twins
DESCRIPTION:Tomasz Bednarz and Ram Cherukuri (NVIDIA)\n\nIf you always wan
 ted to have a magic framework under your hands that allows you to easily b
 lend physics, as expressed by governing partial differential equations, bo
 undary conditions, and training data to build high-fidelity, parameterized
 , surrogate deep learning models, applicable to solve problems across doma
 ins, then this course is definitely not to miss. The course will introduce
  you to the Modulus platform that abstracts the complexity of setting up a
  scalable training pipeline, so you can leverage your domain expertise to 
 map problems to an AI model’s training and develop better neural network a
 rchitectures. The platform offers a variety of approaches for training phy
 sics-based neural network models, from purely physics-driven models with p
 hysics-informed neural networks (PINNs) to physics-based, data-driven arch
 itectures such as neural operators. You will learn how to apply it to simu
 late use cases applicable across science and engineering domains, and grap
 hics. You will be able to put development of your digital twins that inclu
 de physics simulations to the next level. Many use cases will show you how
  widely same framework can be applied.\n\nRegistration Category: Full Acce
 ss\n\n
URL:https://asia.siggraph.org/2023/full-program?id=crs_111&sess=sess103
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