Stiff neural ordinary differential equations
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Publication:6556966
DOI10.1063/5.0060697zbMATH Open1546.37134MaRDI QIDQ6556966
Suyong Kim, S. Deng, Christopher Rackauckas, Yingbo Ma, Weiqi Ji
Publication date: 17 June 2024
Published in: Chaos (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Neural networks for/in biological studies, artificial life and related topics (92B20) Time series analysis of dynamical systems (37M10)
Cites Work
- Title not available (Why is that?)
- SUNDIALS
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Time-series learning of latent-space dynamics for reduced-order model closure
- FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs
- Julia: A Fresh Approach to Numerical Computing
- A User’s View of Solving Stiff Ordinary Differential Equations
- Differential/Algebraic Equations are not ODE’<scp>s</scp>
- Gauss–Seidel Iteration for Stiff ODES from Chemical Kinetics
- Convergence analysis of Krylov subspace methods
- Stiff neural ordinary differential equations
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