ArticleMultiscale modeling & simulation : a SIAM interdisciplinary journal2021
WEAK SINDy: GALERKIN-BASED DATA-DRIVEN MODEL SELECTION.
Article in Multiscale modeling & simulation : a SIAM interdisciplinary journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Exploring memory effects: Sparse identification in vector-borne diseases.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.Bulletin of mathematical biology · 2026Article
- Article
- Reconstructing Actin Dynamics of the Leading Edge from Observational Data.bioRxiv : the preprint server for biology · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Article
- Learning structured population models from data with WSINDy.PLoS computational biology · 2025Article
- SINDy-RL for interpretable and efficient model-based reinforcement learning.Nature communications · 2025Article
- WEAK FORM LEARNING FOR MEAN-FIELD PARTIAL DIFFERENTIAL EQUATIONS: AN APPLICATION TO INSECT MOVEMENT.ArXiv · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
- Article
- Data-driven model discovery and model selection for noisy biological systems.PLoS computational biology · 2025Article
- Data-driven analysis of anomalous transport and three-wave-coupling effects in anJournal of electric propulsion · 2025Article
- Weak-form inference for hybrid dynamical systems in ecology.Journal of the Royal Society, Interface · 2024Article
- Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data.Journal of computational physics · 2024Article
- Coarse-graining Hamiltonian systems using WSINDy.Scientific reports · 2024Article
- From biological data to oscillator models using SINDy.iScience · 2024Article
- Weighted Composition Operators for Learning Nonlinear Dynamics.IFAC-PapersOnLine · 2024Article
- OKRidge: Scalable Optimal k-Sparse Ridge Regression.Advances in neural information processing systems · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present a novel weak formulation and discretization for discovering governing equations from noisy measurement data. This method of learning differential equations from data fits into a new class of algorithms that replace pointwise derivative approximations with linear transformations and variance reduction techniques. Compared to the standard SINDy algorithm presented in [S. L. Brunton, J. L. Proctor, and J. N. Kutz,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.