Department of Mechanical and Aerospace Engineering (MAE) Seminar
Ming-Cheng Cheng
Electrical and Computer Engineering, Clarkson University
Will present a talk titled:
Physics Simulations Enabled by Projection-based Learning and Domain Decomposition for Engineering and Science Applications
Abstract: A physics-based learning methodology based on proper orthogonal decomposition (POD) has been investigated for simulation of physical problems in several areas in engineering and science. Using this methodology, a physical problem of interest is projected onto a mathematical space constituted by a set of basis functions (or POD modes) that are trained by solution data collected from direct numerical simulations of the governing equation. The POD simulation model is then closed by the Galerkin projection of the governing equation onto the generated POD modes, which accounts for the physical principles enforced by the governing equation and offers an accurate prediction beyond the training conditions. This is very different from most machine learning methods solving physical quantities, relying only on statistical learning approaches to minimize the deviation of the prediction without considering physical principles. Such a learning approach often leads to a prediction inconsistent with the underline physical principles in case of extrapolation. The major drawback of the POD-Galerkin simulation methodology is its enormous computational effort needed to collect training data and to prepare POD model parameters for large-scale domains with fine resolution. To overcome this issue, domain decomposition is applied to partition large structures into smaller standard building blocks, and the interior penalty discontinuous Galerkin method is applied to minimize the discontinuities at the block interfaces. In this presentation, this simulation methodology is applied to thermal analysis in CPUs, the quantum eigenvalue problem in nanostructures, and electromagnetic simulation in a resonator.
Date: March 31, 2023
Location: CAMP 176
Time: 11:00 am
ZOOM Link for virtual attendance
https://clarkson.zoom.us/j/98947209769
Bio: Ming-Cheng Cheng received the B.S. degree in Electrophysics from National Chiao-Tung University, Hsinchu, Taiwan, and the Ph.D. degree in Electrical Engineering from Polytechnic University, Brooklyn, NY. He is currently a professor in Electrical and Computer Engineering at Clarkson University, Potsdam, NY. His research has covered a wide range of areas including macroscopic and microscopic transport modeling of solid-state devices, spin-polarized electron transport, electro-thermal simulation of semiconductor devices and integrated circuits, optimization of solid-state devices, electromagnetic simulation for core losses in magnetic materials, etc. Recently, he has devoted his effort to developing effective physics simulation methods and their applications based on physics-informed data-driven learning algorithms for different research areas, including dynamic thermal analysis of CPUs/GPUs, thermal-aware task scheduling in multi-core microprocessors, simulations of quantum eigenvalue problems for nanostructures and materials, DFT simulations, electromagnetic simulations, etc.
