:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Jianhong, Yang, Shihao
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2501.14107
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
by: Li, Zhaohui, et al.
Published: (2022)

dynoGP: Deep Gaussian Processes for dynamic system identification
by: Benavoli, Alessio, et al.
Published: (2025)

HiGP: A high-performance Python package for Gaussian Process
by: Huang, Hua, et al.
Published: (2025)

Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
by: Chen, Yingyi, et al.
Published: (2024)

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

Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
by: Chen, Jianhong, et al.
Published: (2026)

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
by: Abu-Shaira, Mohammad, et al.
Published: (2025)

Physics-Informed Inference Time Scaling for Solving High-Dimensional PDE via Defect Correction
by: Fan, Zexi, et al.
Published: (2025)

Exact Inference for Continuous-Time Gaussian Process Dynamics
by: Ensinger, Katharina, et al.
Published: (2023)

Decoupled-Value Attention for Prior-Data Fitted Networks: GP Inference for Physical Equations
by: Sharma, Kaustubh, et al.
Published: (2025)

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

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

Regular Fourier Features for Nonstationary Gaussian Processes
by: Jawaid, Arsalan, et al.
Published: (2026)

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

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)

On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
by: Liu, Yang, et al.
Published: (2026)

Preconditioned Additive Gaussian Processes with Fourier Acceleration
by: Wagner, Theresa, et al.
Published: (2025)

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
by: Shi, Xinxing, et al.
Published: (2024)

Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
by: Zhong, Ming, et al.
Published: (2024)

Fourier Feature Pyramids for Physics-Informed Neural Networks
by: Zhao, Brandon, et al.
Published: (2026)

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
by: Cheema, Talay M, et al.
Published: (2023)

EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inference
by: Kaushik, Prakhar, et al.
Published: (2025)

Three Costs of Amortizing Gaussian Process Inference with Neural Processes
by: Young, Robin
Published: (2026)

Sparse Orthogonal Variational Inference for Gaussian Processes
by: Shi, Jiaxin, et al.
Published: (2019)

Amortized Variational Inference for Deep Gaussian Processes
by: Meng, Qiuxian, et al.
Published: (2024)

Instrumental and Proximal Causal Inference with Gaussian Processes
by: Zhang, Yuqi, et al.
Published: (2026)

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach
by: Groun, Nourelhouda, et al.
Published: (2024)

DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
by: Bolgár, Bence, et al.
Published: (2025)

Bayesian Causal Inference with Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2024)

Diffusion Bridge Variational Inference for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2025)

Lightweight Gaussian Process Inference in C++ on Metal and CUDA
by: Fang, Yu-Hsueh
Published: (2026)

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

Backward Reconstruction of the Chafee--Infante Equation via Physics-Informed WGAN-GP
by: Shomberg, Joseph L.
Published: (2026)

Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics
by: Zhang, Xizhuo, et al.
Published: (2025)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
by: Wu, Yulun, et al.
Published: (2025)

Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
by: Yang, Zewen, et al.
Published: (2024)

Physics-Informed Neural Nets for Control of Dynamical Systems
by: Antonelo, Eric Aislan, et al.
Published: (2021)