Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion

5 August 2022

Papers citing "Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion"

9 / 9 papers shown

Title
OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics Rui Zhang Qi Meng Han Wan Yang Liu Zhi-Ming Ma Hao Sun AI4CE 99 0 0 12 Jun 2025
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Hao Xu Y. Chen Rui Cao Tianning Tang Mengge Du Jiacheng Li Adrian H. Callaghan Dongxiao Zhang 70 0 0 09 May 2025
ViSymRe: Vision-guided Multimodal Symbolic Regression Da Li Junping Yin Jin Xu Xinxin Li Juan Zhang 125 1 0 15 Dec 2024
Discovery and inversion of the viscoelastic wave equation in inhomogeneous media Su Chen Yi Ding Hiroe Miyake Xiaojun Li 113 0 0 27 Sep 2024
Towards stable real-world equation discovery with assessing differentiating quality influence Mikhail Masliaev Ilya Markov Alexander Hvatov 29 0 0 09 Nov 2023
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data Mengge Du Yuntian Chen Longfeng Nie Siyu Lou Dong-juan Zhang AI4CE 83 8 0 14 Sep 2023
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data R. Stephany Christopher Earls 59 4 0 09 Sep 2023
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery Pongpisit Thanasutives Takashi Morita M. Numao Ken-ichi Fukui 85 2 0 20 Aug 2023
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data R. Stephany Christopher Earls 44 18 0 09 Dec 2022