A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks

15 July 2020

Papers citing "A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks"

26 / 26 papers shown

Title
Is the end of Insight in Sight ? J. Tucny M. Durve S. Succi 74 0 0 07 May 2025
Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks Mehran Mazandarani Marzieh Najariyan PINN AI4CE 37 0 0 02 May 2025
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations Hamidreza Eivazi Jendrik-Alexander Tröger Stefan H. A. Wittek Stefan Hartmann Andreas Rausch AI4CE 51 0 0 27 Mar 2025
Transport-Embedded Neural Architecture: Redefining the Landscape of physics aware neural models in fluid mechanics Amirmahdi Jafari 38 0 0 05 Oct 2024
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems A. Thiruthummal Sergiy Shelyag Eun-Jin Kim 38 2 0 07 Jun 2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be? Jorge F. Urbán P. Stefanou José A. Pons PINN 50 6 0 07 May 2024
BiLO: Bilevel Local Operator Learning for PDE inverse problems Ray Zirui Zhang Xiaohui Xie John S. Lowengrub 73 1 0 27 Apr 2024
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations Hongyan Li Song Jiang Wenjun Sun Liwei Xu Guanyu Zhou 45 2 0 04 Mar 2024
Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks Pratanu Roy Stephen T Castonguay PINN AI4TS 57 9 0 15 Feb 2024
Approximation of Solution Operators for High-dimensional PDEs Nathan Gaby Xiaojing Ye 32 0 0 18 Jan 2024
Residual-based attention and connection to information bottleneck theory in PINNs Sokratis J. Anagnostopoulos Juan Diego Toscano Nikos Stergiopulos George Karniadakis 40 20 0 01 Jul 2023
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time Y. Qian Yongchao Zhang Yuanfei Huang S. Dong PINN 31 1 0 22 Mar 2023
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 18 12 0 19 Oct 2022
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs Birgit Hillebrecht B. Unger PINN 23 5 0 07 Oct 2022
Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems Qihong Yang Yangtao Deng Yu Yang Qiaolin He Shiquan Zhang 24 13 0 22 Sep 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu Min Zhu Qinyan Tan Yadhu Kartha Lu Lu 37 357 0 21 Jul 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz Pedram Hassanzadeh PINN 33 45 0 05 May 2022
Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine Naxian Ni S. Dong 34 20 0 24 Apr 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 52 23 0 11 Apr 2022
Respecting causality is all you need for training physics-informed neural networks Sizhuang He Shyam Sankaran P. Perdikaris PINN CML AI4CE 58 199 0 14 Mar 2022
Numerical Approximation of Partial Differential Equations by a Variable Projection Method with Artificial Neural Networks S. Dong Jielin Yang 50 17 0 24 Jan 2022
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 38 451 0 01 Nov 2021
A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines S. Dong Zongwei Li 33 29 0 14 Mar 2021
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations Jianfeng Lu Yulong Lu Min Wang 36 37 0 05 Jan 2021
Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations S. Dong Zongwei Li 36 164 0 04 Dec 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 159 1,344 0 27 Aug 2019