Image de couverture de livre

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

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sun, Xiangyu, Hosseinmardi, Shirin, Yousefpour, Amin, Bostanabad, Ramin
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
par: Yousefpour, Amin, et autres
Publié: (2025)

Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
par: Yousefpour, Amin, et autres
Publié: (2024)

Compliance Minimization via Physics-Informed Gaussian Processes
par: Sun, Xiangyu, et autres
Publié: (2025)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
par: Mora, Carlos, et autres
Publié: (2024)

Operator Learning with Gaussian Processes
par: Mora, Carlos, et autres
Publié: (2024)

Learning Mappings in Mesh-based Simulations
par: Hosseinmardi, Shirin, et autres
Publié: (2025)

Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
par: Shishehbor, Mehdi, et autres
Publié: (2024)

GP+: A Python Library for Kernel-based learning via Gaussian Processes
par: Yousefpour, Amin, et autres
Publié: (2023)

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
par: Negarandeh, Nima, et autres
Publié: (2025)

Should We Simultaneously Calibrate Multiple Computer Models?
par: Eweis-Labolle, Jonathan Tammer, et autres
Publié: (2025)

Constrained multi-fidelity Bayesian optimization with automatic stop condition
par: Foumani, Zahra Zanjani, et autres
Publié: (2025)

Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
par: Amiri, Mahsa, et autres
Publié: (2024)

Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
par: Yousefpour, Negin, et autres
Publié: (2024)

A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
par: Vendrell-Gallart, Oriol, et autres
Publié: (2025)

Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
par: Gladstone, Rini Jasmine, et autres
Publié: (2021)

Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
par: Hashem, Tahrima, et autres
Publié: (2024)

On the Uncertainty Quantification Ability of Tabular Foundation Models
par: Johnson, Tyler R., et autres
Publié: (2026)

Physics-informed Gaussian Processes for Safe Envelope Expansion
par: Harp, D. Isaiah, et autres
Publié: (2025)

Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics
par: Zhang, Xizhuo, et autres
Publié: (2025)

Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior
par: Xinming, Wang, et autres
Publié: (2024)

Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors
par: Hossen, Md Abir, et autres
Publié: (2026)

Multi-Output Robust and Conjugate Gaussian Processes
par: Rooijakkers, Joshua, et autres
Publié: (2025)

Time-Varying Gaussian Process Bandits with Unknown Prior
par: Ziomek, Juliusz, et autres
Publié: (2024)

Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
par: Alvi, Sk Md Ahnaf Akif, et autres
Publié: (2025)

Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators
par: Bai, Tianming, et autres
Publié: (2026)

Transformed Latent Variable Multi-Output Gaussian Processes
par: Jiang, Xiaoyu, et autres
Publié: (2026)

Multi-Output Gaussian Processes for Graph-Structured Data
par: Nakai-Kasai, Ayano, et autres
Publié: (2025)

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
par: Sandberg, Jack, et autres
Publié: (2025)

Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
par: Jiang, Xiaoyu, et autres
Publié: (2024)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
par: Tebbe, Jörn, et autres
Publié: (2024)

Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization
par: Chen, Jiangyu, et autres
Publié: (2026)

A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
par: Charusaie, Mohammad-Amin, et autres
Publié: (2024)

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
par: Sendera, Marcin, et autres
Publié: (2024)

Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
par: Tan, Jing, et autres
Publié: (2024)

Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
par: Liu, Haitao, et autres
Publié: (2022)

Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
par: Tanaka, Yusuke, et autres
Publié: (2022)

Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes
par: Gao, Jingyi, et autres
Publié: (2024)

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
par: Yue, Xubo, et autres
Publié: (2021)

Consensus-based Recursive Multi-Output Gaussian Process
par: Rao, Yogesh Prasanna Kumar, et autres
Publié: (2026)

Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
par: Weng, Yidou, et autres
Publié: (2024)