Modeling systems with machine learning based differential equations

DOI10.1016/J.CHAOS.2022.112872OpenAlexW4308943823MaRDI QIDQ2680007

Yanyan Li

Publication date: 26 January 2023

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.05935

zbMATH Keywords

time seriesmachine learningcontinuous models

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Numerical optimization and variational techniques (65K10) Learning and adaptive systems in artificial intelligence (68T05) Time series analysis of dynamical systems (37M10)

Uses Software

• DGM

Cites Work

• Unnamed Item
• Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems
• DGM: a deep learning algorithm for solving partial differential equations
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A kernel-based Adaline for function approximation

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