Deep learning algorithm for data-driven simulation of noisy dynamical system

DOI10.1016/j.jcp.2018.10.024zbMath1416.62552arXiv1802.08323OpenAlexW2788043262MaRDI QIDQ2311511

Kyongmin Yeo, Igor Melnyk

Publication date: 10 July 2019

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.08323

zbMATH Keywords

time seriesrecurrent neural networknonlinear dynamical systemdeep learningdata-driven simulation

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Time series analysis of dynamical systems (37M10) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (10)

Statistical Modeling for Spatio-Temporal Data From Stochastic Convection-Diffusion Processes ⋮ A long short-term memory embedding for hybrid uplifted reduced order models ⋮ Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems ⋮ Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems ⋮ An energy-based deep splitting method for the nonlinear filtering problem ⋮ Data-driven reconstruction of nonlinear dynamics from sparse observation ⋮ Coercing machine learning to output physically accurate results ⋮ On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD ⋮ Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network ⋮ Time-series learning of latent-space dynamics for reduced-order model closure

Uses Software

• EnKF
• forecast
• Torch
• Forecast
• Adam
• PRMLT

Cites Work

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• Synthesis of recurrent neural networks for dynamical system simulation
• Spatio-temporal phenomena in complex systems with time delays
• Oscillation and Chaos in Physiological Control Systems

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