DeepMoD: Deep learning for Model Discovery in noisy data

20 April 2019

Papers citing "DeepMoD: Deep learning for Model Discovery in noisy data"

6 / 6 papers shown

Title
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 136 0 0 02 Mar 2025
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 82 1 0 10 Feb 2025
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification Siva Viknesh Younes Tatari Amirhossein Arzani 66 2 0 21 Oct 2024
Stability selection enables robust learning of partial differential equations from limited noisy data Suryanarayana Maddu B. Cheeseman I. Sbalzarini Christian L. Müller 40 19 0 17 Jul 2019
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 58 611 0 28 Nov 2017
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data Anuj Karpatne G. Atluri James H. Faghmous M. Steinbach A. Banerjee A. Ganguly Shashi Shekhar N. Samatova Vipin Kumar AI4CE 49 978 0 27 Dec 2016