Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data

14 May 2022

Papers citing "Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data"

7 / 7 papers shown

Title
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology Mario De Florio Zongren Zou Daniele E. Schiavazzi George Karniadakis 28 3 0 13 Aug 2024
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography Vincent C. Scholz Yaohua Zang P. Koutsourelakis 41 3 0 30 Jul 2024
A Critical Analysis of the Theoretical Framework of the Extreme Learning Machine Irina Perfilievaa N. Madrid Manuel Ojeda-Aciego Piotr Artiemjew Agnieszka Niemczynowicz 21 0 0 25 Jun 2024
Solving partial differential equations with sampled neural networks Chinmay Datar Taniya Kapoor Abhishek Chandra Qing Sun Iryna Burak Erik Lien Bolager Anna Veselovska Massimo Fornasier Felix Dietrich 35 1 0 31 May 2024
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 34 68 0 30 Sep 2021
Variational Inference at Glacier Scale D. Brinkerhoff BDL 24 11 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 183 759 0 13 Mar 2020