Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1806.11187
Cited By
Neural-net-induced Gaussian process regression for function approximation and PDE solution
22 June 2018
G. Pang
Liu Yang
George Karniadakis
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Neural-net-induced Gaussian process regression for function approximation and PDE solution"
22 / 22 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
78
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
84
0
0
01 Feb 2025
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
Marc Härkönen
Markus Lange-Hegermann
Bogdan Raiță
138
16
0
29 Dec 2022
Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction
Mehrdad Tavassoli
S. Katyara
Maria Pozzi
Nikhil Deshpande
D. Caldwell
D. Prattichizzo
89
13
0
14 Oct 2022
Spiking Neural Operators for Scientific Machine Learning
Adar Kahana
Qian Zhang
Leonard Gleyzer
George Karniadakis
65
9
0
17 May 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
95
15
0
07 Apr 2022
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
64
63
0
16 Mar 2022
On the Correspondence between Gaussian Processes and Geometric Harmonics
Felix Dietrich
J. M. Bello-Rivas
Ioannis G. Kevrekidis
56
3
0
05 Oct 2021
Neural Network Gaussian Processes by Increasing Depth
Shao-Qun Zhang
Fei Wang
Feng-lei Fan
66
7
0
29 Aug 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
240
675
0
20 Mar 2021
Learning elliptic partial differential equations with randomized linear algebra
Nicolas Boullé
Alex Townsend
40
43
0
31 Jan 2021
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications
Xuhui Meng
H. Babaee
George Karniadakis
59
132
0
19 Dec 2020
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Cheolhei Lee
Jianguo Wu
Wei Cao
Xiaowei Yue
52
19
0
21 Nov 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
245
794
0
13 Mar 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
Pengzhan Jin
Zhen Zhang
Aiqing Zhu
Yifa Tang
George Karniadakis
105
21
0
11 Jan 2020
Constraint-Aware Neural Networks for Riemann Problems
Jim Magiera
Deep Ray
J. Hesthaven
C. Rohde
AI4CE
PINN
65
60
0
29 Apr 2019
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
Seungjoon Lee
Felix Dietrich
George Karniadakis
Ioannis G. Kevrekidis
41
38
0
16 Dec 2018
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Xiu Yang
D. Barajas-Solano
G. Tartakovsky
A. Tartakovsky
73
78
0
24 Nov 2018
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
Siyu Dai
Shawn Schaffert
Andreas G. Hofmann
134
367
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
124
412
0
21 Sep 2018
Deep Learning of Vortex Induced Vibrations
M. Raissi
Zhicheng Wang
M. Triantafyllou
George Karniadakis
AI4CE
81
378
0
26 Aug 2018
Parametric Gaussian Process Regression for Big Data
M. Raissi
99
39
0
11 Apr 2017
1