Model Discovery
21 papers with code • 0 benchmarks • 0 datasets
discovering PDEs from spatiotemporal data
Benchmarks
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Most implemented papers
Model discovery in the sparse sampling regime
georgestod/sparsistent_model_disco
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To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interpretable models from coarsely and off-grid sampled observations.
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
In this work, we introduce deep learning autoencoders to discover coordinate transformations that capture the underlying parametric dependence of a dynamical system in terms of its canonical normal form, allowing for a simple representation of the parametric dependence and bifurcation structure.
Sparsistent Model Discovery
georgestod/sparsistent_model_disco
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Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields.
Discovering PDEs from Multiple Experiments
Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations.
A toolkit for data-driven discovery of governing equations in high-noise regimes
Second, we propose a technique, applicable to any model discovery method based on x' = f(x), to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data.
PySINDy: A comprehensive Python package for robust sparse system identification
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community.
Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects
We introduce a discrepancy modeling framework to identify the missing physics and resolve the model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space residual, and (ii) by discovering a model for the deterministic dynamical error.
Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization
Discovering governing equations of complex dynamical systems directly from data is a central problem in scientific machine learning.
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach
The study successfully reconstructed sorption uptake kinetics using sparse and symbolic regression, and accurately predicted breakthrough curves using identified polynomials, highlighting the potential of the proposed framework for discovering sorption kinetic law structures.
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE
jameslu01/tdnode
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We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data.
