Imagen de Portada

Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yousefpour, Amin, Hosseinmardi, Shirin, Mora, Carlos, Bostanabad, Ramin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
por: Yousefpour, Amin, et al.
Publicado: (2025)

Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
por: Sun, Xiangyu, et al.
Publicado: (2026)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
por: Mora, Carlos, et al.
Publicado: (2024)

Compliance Minimization via Physics-Informed Gaussian Processes
por: Sun, Xiangyu, et al.
Publicado: (2025)

Operator Learning with Gaussian Processes
por: Mora, Carlos, et al.
Publicado: (2024)

Learning Mappings in Mesh-based Simulations
por: Hosseinmardi, Shirin, et al.
Publicado: (2025)

Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
por: Shishehbor, Mehdi, et al.
Publicado: (2024)

GP+: A Python Library for Kernel-based learning via Gaussian Processes
por: Yousefpour, Amin, et al.
Publicado: (2023)

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
por: Negarandeh, Nima, et al.
Publicado: (2025)

Should We Simultaneously Calibrate Multiple Computer Models?
por: Eweis-Labolle, Jonathan Tammer, et al.
Publicado: (2025)

Constrained multi-fidelity Bayesian optimization with automatic stop condition
por: Foumani, Zahra Zanjani, et al.
Publicado: (2025)

Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
por: Amiri, Mahsa, et al.
Publicado: (2024)

Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
por: Yousefpour, Negin, et al.
Publicado: (2024)

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

Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
por: Hashem, Tahrima, et al.
Publicado: (2024)

On the Uncertainty Quantification Ability of Tabular Foundation Models
por: Johnson, Tyler R., et al.
Publicado: (2026)

Physics-informed Gaussian Processes for Safe Envelope Expansion
por: Harp, D. Isaiah, et al.
Publicado: (2025)

Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators
por: Bai, Tianming, et al.
Publicado: (2026)

Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity
por: Du, Honghui, et al.
Publicado: (2024)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
por: Tebbe, Jörn, et al.
Publicado: (2024)

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
por: Sendera, Marcin, et al.
Publicado: (2024)

Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
por: Gladstone, Rini Jasmine, et al.
Publicado: (2021)

Deep Gaussian Process Proximal Policy Optimization
por: van der Lende, Matthijs, et al.
Publicado: (2025)

Pre-trained Gaussian Processes for Bayesian Optimization
por: Wang, Zi, et al.
Publicado: (2021)

Vector Optimization with Gaussian Process Bandits
por: Korkmaz, İlter Onat, et al.
Publicado: (2024)

JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling
por: Du, Honghui, et al.
Publicado: (2025)

Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
por: Zhang, Yan, et al.
Publicado: (2026)

Physics-Informed Variational State-Space Gaussian Processes
por: Hamelijnck, Oliver, et al.
Publicado: (2024)

Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
por: Wei, Yunyue, et al.
Publicado: (2024)

PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders
por: Spitieris, Michail, et al.
Publicado: (2025)

Physics-Embedded Gaussian Process for Traffic State Estimation
por: Chen, Yanlin, et al.
Publicado: (2025)

Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS
por: Shoham, Neta, et al.
Publicado: (2025)

Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization
por: Li, Kevin, et al.
Publicado: (2025)

LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization
por: Zhou, Yuanye, et al.
Publicado: (2025)

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
por: S., Sindu B., et al.
Publicado: (2026)

Quantum Gaussian Process Regression for Bayesian Optimization
por: Rapp, Frederic, et al.
Publicado: (2023)

Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
por: Li, Yezhuo, et al.
Publicado: (2025)

Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
por: Ha, Huong, et al.
Publicado: (2023)

Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks
por: Candelieri, Antonio, et al.
Publicado: (2026)

Gaussian Process Regression -- Neural Network Hybrid with Optimized Redundant Coordinates
por: Manzhos, Sergei, et al.
Publicado: (2025)