Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Yousefpour, Amin, Hosseinmardi, Shirin, Sun, Xiangyu, Bostanabad, Ramin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
by: Sun, Xiangyu, et al.
Published: (2026)
by: Sun, Xiangyu, et al.
Published: (2026)
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
by: Yousefpour, Amin, et al.
Published: (2024)
by: Yousefpour, Amin, et al.
Published: (2024)
Compliance Minimization via Physics-Informed Gaussian Processes
by: Sun, Xiangyu, et al.
Published: (2025)
by: Sun, Xiangyu, et al.
Published: (2025)
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024)
by: Mora, Carlos, et al.
Published: (2024)
Operator Learning with Gaussian Processes
by: Mora, Carlos, et al.
Published: (2024)
by: Mora, Carlos, et al.
Published: (2024)
Learning Mappings in Mesh-based Simulations
by: Hosseinmardi, Shirin, et al.
Published: (2025)
by: Hosseinmardi, Shirin, et al.
Published: (2025)
Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
by: Shishehbor, Mehdi, et al.
Published: (2024)
by: Shishehbor, Mehdi, et al.
Published: (2024)
GP+: A Python Library for Kernel-based learning via Gaussian Processes
by: Yousefpour, Amin, et al.
Published: (2023)
by: Yousefpour, Amin, et al.
Published: (2023)
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
by: Negarandeh, Nima, et al.
Published: (2025)
by: Negarandeh, Nima, et al.
Published: (2025)
Should We Simultaneously Calibrate Multiple Computer Models?
by: Eweis-Labolle, Jonathan Tammer, et al.
Published: (2025)
by: Eweis-Labolle, Jonathan Tammer, et al.
Published: (2025)
Constrained multi-fidelity Bayesian optimization with automatic stop condition
by: Foumani, Zahra Zanjani, et al.
Published: (2025)
by: Foumani, Zahra Zanjani, et al.
Published: (2025)
Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
by: Amiri, Mahsa, et al.
Published: (2024)
by: Amiri, Mahsa, et al.
Published: (2024)
Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
by: Yousefpour, Negin, et al.
Published: (2024)
by: Yousefpour, Negin, et al.
Published: (2024)
A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
by: Vendrell-Gallart, Oriol, et al.
Published: (2025)
by: Vendrell-Gallart, Oriol, et al.
Published: (2025)
Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
by: Hashem, Tahrima, et al.
Published: (2024)
by: Hashem, Tahrima, et al.
Published: (2024)
On the Uncertainty Quantification Ability of Tabular Foundation Models
by: Johnson, Tyler R., et al.
Published: (2026)
by: Johnson, Tyler R., et al.
Published: (2026)
Physics-informed Gaussian Processes for Safe Envelope Expansion
by: Harp, D. Isaiah, et al.
Published: (2025)
by: Harp, D. Isaiah, et al.
Published: (2025)
Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators
by: Bai, Tianming, et al.
Published: (2026)
by: Bai, Tianming, et al.
Published: (2026)
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
by: Zhong, Ming, et al.
Published: (2024)
by: Zhong, Ming, et al.
Published: (2024)
Physics-informed Gaussian Processes as Linear Model Predictive Controller
by: Tebbe, Jörn, et al.
Published: (2024)
by: Tebbe, Jörn, et al.
Published: (2024)
Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
by: Sendera, Marcin, et al.
Published: (2024)
by: Sendera, Marcin, et al.
Published: (2024)
From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
by: Kumar, Sawan, et al.
Published: (2025)
by: Kumar, Sawan, et al.
Published: (2025)
Curriculum Sampling: A Two-Phase Curriculum for Efficient Training of Flow Matching
by: Sun, Pengwei
Published: (2026)
by: Sun, Pengwei
Published: (2026)
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
by: Gladstone, Rini Jasmine, et al.
Published: (2021)
by: Gladstone, Rini Jasmine, et al.
Published: (2021)
Deep Gaussian Process Proximal Policy Optimization
by: van der Lende, Matthijs, et al.
Published: (2025)
by: van der Lende, Matthijs, et al.
Published: (2025)
Pre-trained Gaussian Processes for Bayesian Optimization
by: Wang, Zi, et al.
Published: (2021)
by: Wang, Zi, et al.
Published: (2021)
Discrete Meanflow Training Curriculum
by: Hsu, Chia-Hong, et al.
Published: (2026)
by: Hsu, Chia-Hong, et al.
Published: (2026)
Vector Optimization with Gaussian Process Bandits
by: Korkmaz, İlter Onat, et al.
Published: (2024)
by: Korkmaz, İlter Onat, et al.
Published: (2024)
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
by: Zhang, Xiangyu
Published: (2024)
by: Zhang, Xiangyu
Published: (2024)
Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
by: Zhang, Yan, et al.
Published: (2026)
by: Zhang, Yan, et al.
Published: (2026)
Neural Networks for Generating Better Local Optima in Topology Optimization
by: Herrmann, Leon, et al.
Published: (2024)
by: Herrmann, Leon, et al.
Published: (2024)
Practical Global and Local Bounds in Gaussian Process Regression via Chaining
by: Liu, Junyi, et al.
Published: (2025)
by: Liu, Junyi, et al.
Published: (2025)
PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders
by: Spitieris, Michail, et al.
Published: (2025)
by: Spitieris, Michail, et al.
Published: (2025)
Physics-Embedded Gaussian Process for Traffic State Estimation
by: Chen, Yanlin, et al.
Published: (2025)
by: Chen, Yanlin, et al.
Published: (2025)
Physics-Informed Variational State-Space Gaussian Processes
by: Hamelijnck, Oliver, et al.
Published: (2024)
by: Hamelijnck, Oliver, et al.
Published: (2024)
Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS
by: Shoham, Neta, et al.
Published: (2025)
by: Shoham, Neta, et al.
Published: (2025)
Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization
by: Li, Kevin, et al.
Published: (2025)
by: Li, Kevin, et al.
Published: (2025)
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
by: Wei, Yunyue, et al.
Published: (2024)
by: Wei, Yunyue, et al.
Published: (2024)
LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization
by: Zhou, Yuanye, et al.
Published: (2025)
by: Zhou, Yuanye, et al.
Published: (2025)
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
by: Li, Yezhuo, et al.
Published: (2025)
by: Li, Yezhuo, et al.
Published: (2025)
Similar Items
-
Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
by: Sun, Xiangyu, et al.
Published: (2026) -
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
by: Yousefpour, Amin, et al.
Published: (2024) -
Compliance Minimization via Physics-Informed Gaussian Processes
by: Sun, Xiangyu, et al.
Published: (2025) -
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024) -
Operator Learning with Gaussian Processes
by: Mora, Carlos, et al.
Published: (2024)