B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

13 March 2020

Papers citing "B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data"

5 / 5 papers shown

Title
Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing D. Patel R. Sharma Y.B. Guo AI4CE PINN 15 145 0 02 May 2025
Physics-informed deep learning for infectious disease forecasting Y. Qian Éric Marty Avranil Basu Avranil Basu Eamon B. O'Dea Xianqiao Wang Spencer Fox Pejman Rohani John M. Drake He Li PINN AI4CE 63 64 0 16 Jan 2025
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems Sumanth Kumar Boya Deepak Subramani AI4CE 71 88 0 12 Dec 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 39 27 0 18 Aug 2024
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 240 8,007 0 06 Jun 2015