Modelling spatiotemporal dynamics from Earth observation data with neural differential equations

DOI10.1007/S10994-022-06139-2OpenAlexW4221041518WikidataQ114955306 ScholiaQ114955306MaRDI QIDQ2163266

Emmanuel de Bézenac, Arthur Pajot, Ibrahim Ayed, Patrick Gallinari

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06139-2

zbMATH Keywords

dynamical systemsforecastingspatio-temporalpartial observationsdeep learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

A framework for machine learning of model error in dynamical systems

Uses Software

• PyTorch
• FlowNet
• U-Net
• torchdiffeq
• PDE-Net
• DGM

Cites Work

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• Machine learning of linear differential equations using Gaussian processes
• Deep neural networks motivated by partial differential equations
• 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 proposal on machine learning via dynamical systems
• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
• Robust data-driven discovery of governing physical laws with error bars
• NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
• Deep learning for physical processes: incorporating prior scientific knowledge

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