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

TL;DR

This paper introduces a physics-informed Gaussian process framework for multi-material, multi-physics topology optimization, enabling efficient, high-resolution solutions with sharp interfaces and physically meaningful material distributions.

Contribution

The paper presents a novel PIGP-based approach that models multiple physics and materials simultaneously, overcoming computational challenges and spectral bias in complex topology optimization problems.

Findings

Successfully applied to compliance and heat conduction problems

Achieves super-resolution topologies with sharp interfaces

Accelerates training with new differentiation and integration schemes

Abstract

Machine learning (ML) has been increasingly used for topology optimization (TO). However, most existing ML-based approaches focus on simplified benchmark problems due to their high computational cost, spectral bias, and difficulty in handling complex physics. These limitations become more pronounced in multi-material, multi-physics problems whose objective or constraint functions are not self-adjoint. To address these challenges, we propose a framework based on physics-informed Gaussian processes (PIGPs). In our approach, the primary, adjoint, and design variables are represented by independent GP priors whose mean functions are parametrized via neural networks whose architectures are particularly beneficial for surrogate modeling of PDE solutions. We estimate all parameters of our model simultaneously by minimizing a loss that is based on the objective function, multi-physics potential energy functionals, and design-constraints. We demonstrate the capability of the proposed framework on benchmark TO problems such as compliance minimization, heat conduction optimization, and compliant mechanism design under single- and multi-material settings. Additionally, we leverage thermo-mechanical TO with single- and multi-material options as a representative multi-physics problem. We also introduce differentiation and integration schemes that dramatically accelerate the training process. Our results demonstrate that the proposed PIGP framework can effectively solve coupled multi-physics and design problems simultaneously -- generating super-resolution topologies with sharp interfaces and physically interpretable material distributions. We validate these results using open-source codes and the commercial software package COMSOL.