Bayesian neural networks for weak solution of PDEs with uncertainty quantification

13 January 2021

Papers citing "Bayesian neural networks for weak solution of PDEs with uncertainty quantification"

8 / 8 papers shown

Title
Generating synthetic data for neural operators Erisa Hasani Rachel A. Ward AI4CE 45 7 0 04 Jan 2024
Investigating Deep Learning Model Calibration for Classification Problems in Mechanics S. Mohammadzadeh Peerasait Prachaseree Emma Lejeune AI4CE 34 2 0 01 Dec 2022
Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks Peerasait Prachaseree Emma Lejeune PINN AI4CE 33 11 0 03 Feb 2022
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference Wyatt Bridgman Xiaoxuan Zhang G. Teichert M. Khalil K. Garikipati Reese E. Jones UQCV AI4CE 13 5 0 31 Jan 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 180 758 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 125 508 0 11 Mar 2020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 147 1,339 0 27 Aug 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015