A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency

19 October 2022

Papers citing "A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency"

20 / 20 papers shown

Title
Physics-informed neural networks for inverse problems in supersonic flows Ameya Dilip Jagtap Zhiping Mao Nikolaus Adams George Karniadakis PINN 43 218 0 23 Feb 2022
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 75 467 0 01 Nov 2021
Multi-Objective Loss Balancing for Physics-Informed Deep Learning Rafael Bischof M. Kraus PINN AI4CE 94 98 0 19 Oct 2021
Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics Xiaotian Jiang Danshi Wang Qirui Fan Min Zhang Chao Lu A. Lau AI4CE PINN 39 83 0 01 Sep 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 73 1,187 0 20 May 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 95 516 0 09 Feb 2021
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi Huayu Chen A. Srinivas Rui Qian Nayeon Lee E. D. Cubuk Quoc V. Le Barret Zoph ISeg 290 991 0 13 Dec 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 75 459 0 07 Sep 2020
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks S. Dong Naxian Ni 79 137 0 15 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class of inverse problems for PDEs Siddhartha Mishra Roberto Molinaro PINN 62 266 0 29 Jun 2020
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks K. Shukla P. C. D. Leoni J. Blackshire D. Sparkman George Karniadakis PINN AI4CE 78 232 0 07 May 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 168 534 0 11 Mar 2020
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport Qizhi He D. Barajas-Solano G. Tartakovsky A. Tartakovsky AI4CE 46 262 0 06 Dec 2019
A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems Wei Cai Xiaoguang Li Lizuo Liu 44 85 0 23 Sep 2019
Deep Neural Network Approach to Forward-Inverse Problems Hyeontae Jo Hwijae Son H. Hwang Eunheui Kim PINN 85 28 0 27 Jul 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 97 1,528 0 10 Jul 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 105 408 0 21 Sep 2018
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 91 2,062 0 24 Aug 2017
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 61 788 0 16 Jun 2016
On the number of response regions of deep feed forward networks with piece-wise linear activations Razvan Pascanu Guido Montúfar Yoshua Bengio FAtt 117 257 0 20 Dec 2013