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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
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and BeyondCode1
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model DiscoveryCode1
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODECode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Learning Sparse Nonlinear Dynamics via Mixed-Integer OptimizationCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Sparsely constrained neural networks for model discovery of PDEsCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
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