Neural-net-induced Gaussian process regression for function approximation and PDE solution

22 June 2018

Papers citing "Neural-net-induced Gaussian process regression for function approximation and PDE solution"

15 / 15 papers shown

Title
A general physics-constrained method for the modelling of equation's closure terms with sparse data Tian Chen Shengping Liu Li Liu Heng Yong PINN AI4CE 56 0 0 30 Apr 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng Guang Lin AI4CE 58 0 0 01 Feb 2025
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression S. Z. Ashtiani Mohammad Sarabian K. Laksari H. Babaee 34 2 0 14 Mar 2024
A physics-informed neural network framework for modeling obstacle-related equations Hamid EL Bahja J. C. Hauffen P. Jung B. Bah Issa Karambal PINN AI4CE 44 4 0 07 Apr 2023
Neural Partial Differential Equations with Functional Convolution Z. Wu Xingzhe He Yijun Li Cheng Yang Rui Liu S. Xiong Bo Zhu 25 1 0 10 Mar 2023
Random Grid Neural Processes for Parametric Partial Differential Equations Arnaud Vadeboncoeur Ieva Kazlauskaite Y. Papandreou F. Cirak Mark Girolami Ömer Deniz Akyildiz AI4CE 40 11 0 26 Jan 2023
Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction Mehrdad Tavassoli S. Katyara Maria Pozzi Nikhil Deshpande D. Caldwell D. Prattichizzo 38 11 0 14 Oct 2022
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems Jiahao Zhang Shiqi Zhang Guang Lin 25 14 0 07 Apr 2022
$Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations$ Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations Ling Guo Hao Wu Xiao-Jun Yu Tao Zhou PINN AI4CE 32 58 0 16 Mar 2022
On the Correspondence between Gaussian Processes and Geometric Harmonics Felix Dietrich J. M. Bello-Rivas Ioannis G. Kevrekidis 34 3 0 05 Oct 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 61 655 0 20 Mar 2021
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 21 21 0 11 Jan 2020
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence Xiu Yang D. Barajas-Solano G. Tartakovsky A. Tartakovsky 25 77 0 24 Nov 2018
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 26 355 0 05 Nov 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 27 400 0 21 Sep 2018