PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data

9 December 2022

Papers citing "PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data"

6 / 6 papers shown

Title
CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations Boqian Zhang Juanmian Lei Guoyou Sun Shuaibing Ding Jian Guo 36 0 0 10 Apr 2025
Mechanistic PDE Networks for Discovery of Governing Equations Adeel Pervez E. Gavves Francesco Locatello AI4CE 77 1 0 25 Feb 2025
Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks Michail Koumpanakis Ricardo Vilalta AI4CE 83 0 0 29 Nov 2024
Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid Subhankar Sarkar Souvik Chakraborty 25 0 0 01 Sep 2024
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data R. Stephany Christopher Earls 19 4 0 09 Sep 2023
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder Christophe Bonneville Youngsoo Choi Debojyoti Ghosh Jonathan Belof AI4CE 22 18 0 10 Aug 2023