Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

DOI10.1137/22m148375xarXiv2203.05164OpenAlexW4391224510WikidataQ129320017 ScholiaQ129320017MaRDI QIDQ6192109

Unnamed Author, J. Nathan Kutz, Unnamed Author

Publication date: 12 February 2024

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.05164

zbMATH Keywords

data-driven modelingdiscrepancy modelingmodel discovery

Mathematics Subject Classification ID

Gaussian processes (60G15) Artificial neural networks and deep learning (68T07) Dynamical systems in control (37N35) Time series analysis of dynamical systems (37M10)

Cites Work

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• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Numerical differentiation of noisy, nonsmooth data
• Neural networks and dynamical systems
• Supervised learning from noisy observations: combining machine-learning techniques with data assimilation
• Machine learning for prediction with missing dynamics
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• On dynamic mode decomposition: theory and applications
• On stable numerical differentiation
• Bayesian Calibration of Computer Models
• An Introduction to Fluid Dynamics
• Variable Projection Methods for an Optimized Dynamic Mode Decomposition
• Data-Driven Science and Engineering
• Autodifferentiable Ensemble Kalman Filters
• Shallow neural networks for fluid flow reconstruction with limited sensors
• Data-driven discovery of coordinates and governing equations
• Data Assimilation
• Filtering Complex Turbulent Systems
• Advanced Lectures on Machine Learning
• Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
• Numerical Differentiation and Regularization
• A framework for machine learning of model error in dynamical systems

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