1 citations · 3 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
GP+: A Python Library for Kernel-based learning via Gaussian Processes
Amin Yousefpour, Zahra Zanjani Foumani, Mehdi Shishehbor +2
In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterize…
On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization
Zahra Zanjani Foumani, Amin Yousefpour, Mehdi Shishehbor +1
Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acqu…