AutoIP: A United Framework to Integrate Physics into Gaussian Processes

AutoIP: A United Framework to Integrate Physics into Gaussian Processes

24 February 2022

Aditi S. Krishnapriyan

Robert M. Kirby

Michael W. Mahoney

Papers citing "AutoIP: A United Framework to Integrate Physics into Gaussian Processes"

12 / 12 papers shown

Title
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions Takeshi Koshizuka Issei Sato AI4CE 112 0 0 31 Jan 2025
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 31 0 0 20 Sep 2024
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 65 1 0 21 May 2024
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems Rafael Anderka M. Deisenroth So Takao 20 1 0 26 Feb 2024
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration Xilong Zhao Siyuan Bian Yaoyun Zhang Yuliang Zhang Qinying Gu Xinbing Wang Cheng Zhou Nanyang Ye 29 1 0 18 Dec 2023
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes Shikai Fang Madison Cooley Da Long Shibo Li R. Kirby Shandian Zhe 40 4 0 08 Nov 2023
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels Da Long Wei W. Xing Aditi S. Krishnapriyan R. Kirby Shandian Zhe Michael W. Mahoney 18 0 0 09 Oct 2023
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology Qingsong Xu Yilei Shi Jonathan Bamber Ye Tuo Ralf Ludwig Xiao Xiang Zhu AI4CE 20 10 0 08 Oct 2023
A spectrum of physics-informed Gaussian processes for regression in engineering E. Cross T. Rogers D. J. Pitchforth S. Gibson Matthew R. Jones 24 8 0 19 Sep 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 34 17 0 08 May 2023
Random Grid Neural Processes for Parametric Partial Differential Equations A. Vadeboncoeur Ieva Kazlauskaite Y. Papandreou F. Cirak Mark Girolami Ömer Deniz Akyildiz AI4CE 28 11 0 26 Jan 2023
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015