$Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations$

Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations

16 March 2022

Papers citing "Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations"

6 / 6 papers shown

Title
Physics-Informed Solution of The Stationary Fokker-Plank Equation for a Class of Nonlinear Dynamical Systems: An Evaluation Study H. Alhussein Mohammed Khasawneh M. Daqaq 23 1 0 25 Sep 2023
Physics-guided training of GAN to improve accuracy in airfoil design synthesis Kazunari Wada Katsuyuki Suzuki Kazuo Yonekura AI4CE 29 11 0 19 Aug 2023
$Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion$ Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion Xiongbin Yan Zhi-Qin John Xu Zheng Ma 32 2 0 03 Apr 2023
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems Xiongbin Yan Z. Xu Zheng Ma 14 2 0 22 Nov 2022
Failure-informed adaptive sampling for PINNs Zhiwei Gao Liang Yan Tao Zhou 18 77 0 01 Oct 2022
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020