Announcement Detail

Monday, December 22, 2025

11am PST

Join via Zoom: https://us06web.zoom.us/j/82464478256?pwd=ZMkJVFdjMJzadgnVWFPqsdUSs4qTaY.1
 

Student Chapter Seminar Series

Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors

Speaker

Alex (Xiangyu) Sun, University of California, Irvine

Abstract:

Traditional ML-based topology optimization (TO) methods suffer from high computational cost, spectral bias, and limited applicability to multi-material, multi-physics problems with non–self-adjoint objectives. To overcome these limitations, a mesh-free physics-informed Gaussian process (GP) framework for TO is introduced.

 ØRepresents the primary, adjoint, and design variables using independent GP priors, whose mean functions are parameterized by neural networks (NNs).

 ØEstimates all parameters simultaneously by minimizing a unified loss constructed from adjoint objectives, potential energy functionals, and design constraints.

 ØEffectively solves multi-physics, multi-material TO problems, producing super-resolution topologies with sharp interfaces and physically interpretable material

Bio:

Dr. Alex (Xiangyu) Sun is a postdoctoral scholar in Mechanical and Aerospace Engineering at the University of California, Irvine, supervised by Prof. Ramin Bostanabad. His current research integrates physics-informed machine learning, computational mechanics, and design optimization to enable efficient and interpretable topology optimization. Prior to UCI, Dr. Sun worked as a research scientist at the Corning Corp, and as a postdoctoral fellow at the University of Wisconsin–Madison.

He earned his Ph.D. in Mechanical Engineering from Johns Hopkins University. Dr. Sun’s work spans scientific machine learning, multi-physics design, and high-rate experimental mechanics under extreme loading. His research vision is to establish an AI-enabled framework that unifies experimental methodologies and computational tools for designing engineered material–structure systems that perform reliably under extreme environments for aerospace and defense applications.