Machine learning for prediction with missing dynamics
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Publication:2128320
DOI10.1016/j.jcp.2020.109922OpenAlexW2979313281MaRDI QIDQ2128320
Shixiao W. Jiang, John Harlim, Senwei Liang, Haizhao Yang
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.05861
Related Items (20)
Linear response based parameter estimation in the presence of model error ⋮ Autodifferentiable Ensemble Kalman Filters ⋮ The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation ⋮ Data-driven sparse identification of nonlinear dynamical systems using linear multistep methods ⋮ Simultaneous neural network approximation for smooth functions ⋮ Reduced-order autodifferentiable ensemble Kalman filters ⋮ Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations ⋮ A framework for machine learning of model error in dynamical systems ⋮ Regression-Based Projection for Learning Mori–Zwanzig Operators ⋮ Reservoir computing with error correction: long-term behaviors of stochastic dynamical systems ⋮ Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations ⋮ Learning Theory for Dynamical Systems ⋮ A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics ⋮ The Mori-Zwanzig formulation of deep learning ⋮ Deep neural network based adaptive learning for switched systems ⋮ Physics-constrained data-driven variational method for discrepancy modeling ⋮ Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects ⋮ Kernel-based prediction of non-Markovian time series ⋮ Kernel Analog Forecasting: Multiscale Test Problems ⋮ Learning stochastic dynamics with statistics-informed neural network
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