Predicting shallow water dynamics using echo-state networks with transfer learning

DOI10.1007/s13137-022-00210-9OpenAlexW4308350471MaRDI QIDQ2677718

XiaoQian Chen, Balasubramanya T. Nadiga, Ilya Timofeyev

Publication date: 5 January 2023

Published in: GEM - International Journal on Geomathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.09182

zbMATH Keywords

shallow water equationstransfer learningreservoir computingecho-state networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)

Uses Software

• PDE-Net

Cites Work

• A simple well-balanced and positive numerical scheme for the shallow-water system
• Re-visiting the echo state property
• Data-driven discovery of PDEs in complex datasets
• Optimization and applications of echo state networks with leaky- integrator neurons
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Tikhonov Regularization and Total Least Squares
• On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD
• MODEL STRUCTURAL INFERENCE USING LOCAL DYNAMIC OPERATORS
• DL-PDE: Deep-Learning Based Data-Driven Discovery of Partial Differential Equations from Discrete and Noisy Data
• Data-driven discovery of coordinates and governing equations
• Echo State Property Linked to an Input: Exploring a Fundamental Characteristic of Recurrent Neural Networks

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