ArticleProceedings. Mathematical, physical, and engineering sciences2022
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Beyond empirical models: Discovering constitutive laws in solids with graph-based equation discovery.Science advances · 2026Article
- Explainable AI: learning from the learners.Nature communications · 2026Review
- Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks.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
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Review
- 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
- Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs.ArXiv · 2025Article
- Modeling Moose-Wolf interactions in Isle Royale National Park using sparseidentification of nonlinear dynamics.Scientific reports · 2025Article
- Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.Scientific reports · 2025Article
- Dynamical systems theory as an organizing principle for single-cell biology.NPJ systems biology and applications · 2025Review
- Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series.Entropy (Basel, Switzerland) · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Derivative-Free Domain-Informed Data-Driven Discovery of Sparse Kinetic Models.Industrial & engineering chemistry research · 2025Article
- Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea.Scientific reports · 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
- asteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers.Nucleic acids research · 2024Article
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4 authors.
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Abstract
Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world datasets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900 to 1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control.
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