1 citations · 2 across the 9 of their papers we have counts for
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Scalable Stress-Constrained Topology Optimization: A Simultaneous Approach Based on Machine Learning
Xiangyu Sun, Shirin Hosseinmardi, Ramin Bostanabad
Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural…
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…
Operator Learning with Gaussian Processes
Carlos Mora, Amin Yousefpour, Shirin Hosseinmardi +2
Operator learning focuses on approximating mappings between infinite-dimensional spaces of functions, such as $u: Ω_u\right…
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
Amin Yousefpour, Shirin Hosseinmardi, Carlos Mora +1
Topology optimization (TO) provides a principled mathematical approach for optimizing the performance of a structure by designing its material spatial distribution in a pre-defined…
Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
Mehdi Shishehbor, Shirin Hosseinmardi, Ramin Bostanabad
Deep neural networks (DNNs) are increasingly used to solve partial differential equations (PDEs) that naturally arise while modeling a wide range of systems and physical phenomena.…
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…