8 papers
On the Importance of Geometric Nonlinearity and Temperature-Dependent Properties in Multi-Material Thermo-Mechanical Topology Optimization
Shirin Hosseinmardi, Xiangyu Sun, Ramin Bostanabad
Thermo-mechanical compliant devices are commonly designed with small-strain linear elasticity and temperature-independent material properties, even though they might operate hundre…
Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
Xiangyu Sun, Shirin Hosseinmardi, Amin Yousefpour +1
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…
Compliance Minimization via Physics-Informed Gaussian Processes
Xiangyu Sun, Amin Yousefpour, Shirin Hosseinmardi +1
Machine learning (ML) techniques have recently gained significant attention for solving compliance minimization (CM) problems. However, these methods typically provide poor feature…
Learning Mappings in Mesh-based Simulations
Shirin Hosseinmardi, Ramin Bostanabad
Many real-world physics and engineering problems arise in geometrically complex domains discretized by meshes for numerical simulations. The nodes of these potentially irregular me…
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
Amin Yousefpour, Shirin Hosseinmardi, Xiangyu Sun +1
We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs). Our framework endows all design and state variable…
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
Carlos Mora, Amin Yousefpour, Shirin Hosseinmardi +1
Physics-informed machine learning (PIML) has emerged as a promising alternative to conventional numerical methods for solving partial differential equations (PDEs). PIML models are…