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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 1–25 of 87 papers

TitleStatusHype
PySINDy: A comprehensive Python package for robust sparse system identificationCode2
AutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of MindCode2
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and BeyondCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Physics-informed learning of governing equations from scarce dataCode1
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODECode1
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model DiscoveryCode1
Learning Sparse Nonlinear Dynamics via Mixed-Integer OptimizationCode1
Sparsely constrained neural networks for model discovery of PDEsCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Learning Equations from Biological Data with Limited Time SamplesCode0
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equationsCode0
Automated Modeling Method for Pathloss Model DiscoveryCode0
Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)Code0
HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and AnalysisCode0
Interpretable Scientific Discovery with Symbolic Regression: A ReviewCode0
BoxingGym: Benchmarking Progress in Automated Experimental Design and Model DiscoveryCode0
Bi-Level optimization for parameter estimation of differential equations using interpolationCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial OptimizationCode0
A toolkit for data-driven discovery of governing equations in high-noise regimesCode0
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approachCode0
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