Learning Functional Priors and Posteriors from Data and Physics

8 June 2021

Papers citing "Learning Functional Priors and Posteriors from Data and Physics"

30 / 30 papers shown

Title
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations Shaoqian Zhou Wen You Ling Guo Xuhui Meng DiffM MedIm 51 0 0 23 Mar 2025
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis DiffM 43 2 0 15 Sep 2024
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies Arnaud Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 70 0 0 10 Sep 2024
Empowering Bayesian Neural Networks with Functional Priors through Anchored Ensembling for Mechanics Surrogate Modeling Applications Javad Ghorbanian Nicholas Casaprima Audrey Olivier 28 0 0 08 Sep 2024
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise Yilong Hou Xi’an Li Jinran Wu You-Gan Wang 69 1 0 18 Aug 2024
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology Mario De Florio Zongren Zou Daniele E. Schiavazzi George Karniadakis 31 3 0 13 Aug 2024
PINNs for Medical Image Analysis: A Survey C. Banerjee Kien Nguyen Olivier Salvado Truyen Tran Clinton Fookes AI4CE 34 1 0 02 Aug 2024
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks K. Shukla Juan Diego Toscano Zhicheng Wang Zongren Zou George Karniadakis 51 74 0 05 Jun 2024
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning Zongren Zou Tingwei Meng Paula Chen Jérome Darbon George Karniadakis 52 7 0 12 Apr 2024
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction Ziqi Ma Kamyar Azizzadenesheli A. Anandkumar 26 6 0 02 Feb 2024
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators Zongren Zou Xuhui Meng George Karniadakis AI4CE 41 19 0 19 Nov 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 Survey on Physics Informed Reinforcement Learning: Review and Open Problems C. Banerjee Kien Nguyen Clinton Fookes M. Raissi PINN AI4CE 23 9 0 05 Sep 2023
Physics-Informed Computer Vision: A Review and Perspectives C. Banerjee Kien Nguyen Clinton Fookes G. Karniadakis PINN AI4CE 34 29 0 29 May 2023
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes Minglang Yin Zongren Zou Enrui Zhang C. Cavinato J. Humphrey George Karniadakis SyDa MedIm AI4CE 53 11 0 04 May 2023
In-Context Operator Learning with Data Prompts for Differential Equation Problems Liu Yang Siting Liu Tingwei Meng Stanley J. Osher 40 59 0 17 Apr 2023
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading Minglei Lu Ali Mohammadi Zhaoxu Meng Xuhui Meng Gang Li Zhen Li AI4CE 11 12 0 30 Mar 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 34 12 0 18 Jan 2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks Zongren Zou George Karniadakis AI4CE 18 26 0 05 Jan 2023
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks Olga Graf P. Flores P. Protopapas K. Pichara PINN 37 6 0 14 Dec 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 29 18 0 27 Oct 2022
A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models Erbet Costa Almeida C. Rebello M. Fontana L. Schnitman Idelfonso B. R. Nogueira 19 9 0 05 Sep 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 32 36 0 25 Aug 2022
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning Enrui Zhang B. Spronck J. Humphrey George Karniadakis AI4CE 33 9 0 21 Aug 2022
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems K. Linka Amelie Schäfer Xuhui Meng Zongren Zou George Karniadakis E. Kuhl OOD PINN AI4CE 37 110 0 12 May 2022
Machine Learning in Heterogeneous Porous Materials Martha DÉli H. Deng Cedric G. Fraces K. Garikipati L. Graham‐Brady ... H. Tchelepi B. Važić Hari S. Viswanathan H. Yoon P. Zarzycki AI4CE 27 9 0 04 Feb 2022
Energy-Based Models for Functional Data using Path Measure Tilting Jen Ning Lim Sebastian J. Vollmer Lorenz Wolf Andrew Duncan 26 1 0 04 Feb 2022
A physics-informed variational DeepONet for predicting the crack path in brittle materials S. Goswami Minglang Yin Yue Yu G. Karniadakis AI4CE 25 187 0 16 Aug 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 760 0 13 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 362 11,700 0 09 Mar 2017