Hierarchical Deep Learning of Multiscale Differential Equation
Time-Steppers

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

22 August 2020

Steven L. Brunton

Papers citing "Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers"

9 / 9 papers shown

Title
When are dynamical systems learned from time series data statistically accurate? Jeongjin Park Nicole Yang Nisha Chandramoorthy AI4TS 41 4 0 09 Nov 2024
Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations Asif Hamid Danish Rafiq S. A. Nahvi M. A. Bazaz AI4CE 47 1 0 10 Nov 2023
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations Wenqian Chen P. Stinis OOD AI4CE 38 7 0 21 Mar 2023
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Arnulf Jentzen Adrian Riekert Philippe von Wurstemberger 34 1 0 07 Feb 2023
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 36 68 0 30 Sep 2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 38 25 0 11 Sep 2021
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems Craig Gin D. Shea Steven L. Brunton J. Nathan Kutz 18 87 0 31 Dec 2020
Discovery of Governing Equations with Recursive Deep Neural Networks Jia Zhao Jarrod Mau PINN 32 6 0 24 Sep 2020
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 111 357 0 30 Oct 2017