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Model Discovery

discovering PDEs from spatiotemporal data

Papers

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Showing 51–75 of 87 papers

TitleStatusHype
PySINDy: A comprehensive Python package for robust sparse system identificationCode2
Quantum Model-Discovery—0
Causal KL: Evaluating Causal Discovery—0
A toolkit for data-driven discovery of governing equations in high-noise regimesCode0
Pre and Post Counting for Scalable Statistical-Relational Model Discovery—0
Precision and Fitness in Object-Centric Process Mining—0
Discovering PDEs from Multiple ExperimentsCode0
Feature Recommendation for Structural Equation Model Discovery in Process Mining—0
Sparsistent Model DiscoveryCode0
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equationsCode0
Fully differentiable model discovery—0
Model discovery in the sparse sampling regimeCode0
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Neuro-Reachability of Networked Microgrids—0
OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs—0
Sparsely constrained neural networks for model discovery of PDEsCode1
Causal Inductive Synthesis Corpus—0
Discovery of Governing Equations with Recursive Deep Neural Networks—0
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Learning Equations from Biological Data with Limited Time SamplesCode0
Physics-informed learning of governing equations from scarce dataCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Computational model discovery with reinforcement learning—0
DeepMoD: Deep learning for Model Discovery in noisy dataCode1
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
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