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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 1–10 of 87 papers

TitleStatusHype
AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of MindCode2
PySINDy: A comprehensive Python package for robust sparse system identificationCode2
Learning Sparse Nonlinear Dynamics via Mixed-Integer OptimizationCode1
Physics-informed learning of governing equations from scarce dataCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODECode1
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