Speaker: Sanjay Choudhry, Ph.D.
Faculty Host: Jian Tao, TEES/TAMIDS/HPRC/ECE/MTDE
Abstract: There is an ever-growing body of work using neural networks to solve partial differential equations (PDEs) often referred to as Physics Informed Neural Networks (PINNs). Despite the considerable interest in this field, there has been little success in solving complex problems beyond simple benchmarks. In this talk, we will present SimNet (http://developer.nvidia.com/simnet), an AI-driven multi-physics simulations framework that improves on existing work to handle real-world engineering problems. We’ll also review the convergence of various neural network architectures and training methodologies that allow for solving multi-physics problems with complex geometries. Compared to traditional numerical solvers, SimNet offers a wider range of use case addressability including coupled forward, inverse, and data assimilation problems, and is generalizable to multiple configurations enabled through network parameterization. SimNet offers fast turnaround time by enabling parameterized system representation that solves for multiple configurations simultaneously, as opposed to the traditional solvers that need to solve for one configuration at a time. SimNet is highly customizable and developer-friendly, with APIs that enable customization of geometry, physics, and network architecture. It is optimized for high-performance GPU computing and offers scalable performance for multi-GPU and multi-Node implementation with accelerated linear algebra as well as both FP32 and TF32 computations. Several use cases will be discussed that range from challenging forward multi-physics simulations with turbulence and complex 3D geometries to time-consuming industrial design optimization and inverse problems that are either not addressed at all or addressed inefficiently by the traditional solvers. In particular, we present the solution of conjugate heat transfer problems for heat sinks used to cool the next generation of DGX servers.
Biography: Sanjay Choudhry is a Senior Director at NVIDIA and leads development in AI-driven Scientific Computing & Engineering. Sanjay holds a B.Tech. in Engineering from IIT, Kanpur, M.S. & Ph.D. from The Ohio State University and an M.B.A. from UC, Berkeley. He has worked in the areas of computational science & engineering, AI in engineering, cloud-based HPC, and visualization. He has led the development of several market-leading simulation software and has over 40 publications in the area of computational science & engineering.