69 citations · 189 across the 30 of their papers we have counts for
20 papers · 1 filter
Scalable Stress-Constrained Topology Optimization: A Super-resolution and 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…
Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
Oriol Vendrell-Gallart, Nima Negarandeh, Ramin Bostanabad
Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric…
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…
Should We Simultaneously Calibrate Multiple Computer Models?
Jonathan Tammer Eweis-Labolle, Tyler Johnson, Xiangyu Sun +1
In an increasing number of applications designers have access to multiple computer models which typically have different levels of fidelity and cost. Traditionally, designers calib…
A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
Oriol Vendrell-Gallart, Nima Negarandeh, Zahra Zanjani Foumani +3
Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the poten…
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…
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
Nima Negarandeh, Carlos Mora, Ramin Bostanabad
Gaussian processes (GPs) are powerful probabilistic models that define flexible priors over functions, offering strong interpretability and uncertainty quantification. However, GP…
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…
Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
Mahsa Amiri, Zahra Zanjani Foumani, Penghui Cao +2
Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving re…
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…
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…
Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications
Jonathan Tammer Eweis-LaBolle, Chuanning Zhao, Yoonjin Won +1
As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. Two-phase cooling methods enhanced…
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…
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Arthur Feeney, Zitong Li, Ramin Bostanabad +1
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains. Its unique approach leverages pre-trained networks on sma…
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets
Carlos Mora, Jonathan Tammer Eweis-Labolle, Tyler Johnson +2
In many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced…
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Hengjie Wang, Robert Planas, Aparna Chandramowlishwaran +1
Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of…