Model Discovery
21 papers with code • 0 benchmarks • 0 datasets
discovering PDEs from spatiotemporal data
Benchmarks
These leaderboards are used to track progress in Model Discovery
Most implemented papers
DeepMoD: Deep learning for Model Discovery in noisy data
PhIMaL/DeePyMoD
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We introduce DeepMoD, a Deep learning based Model Discovery algorithm.
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
dynamicslab/modified-SINDy
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The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data.
A new family of Constitutive Artificial Neural Networks towards automated model discovery
For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading.
Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization
We present a flexible data-driven method for dynamical system analysis that does not require explicit model discovery.
DeepArchitect: Automatically Designing and Training Deep Architectures
negrinho/deep_architect_legacy
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ICLR 2018
In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert.
Bayesian differential programming for robust systems identification under uncertainty
PredictiveIntelligenceLab/BayesianDifferentiableProgramming
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This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems.
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines.
Learning Equations from Biological Data with Limited Time Samples
biomathlab/PDE-Learning-few-time-samples
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Equation learning methods present a promising tool to aid scientists in the modeling process for biological data.
Sparsely constrained neural networks for model discovery of PDEs
PhIMaL/DeePyMoD
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Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set.
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
PredictiveIntelligenceLab/GP-NODEs
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This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems.
