ArticleProceedings of the National Academy of Sciences of the United States of America2023
Learning hydrodynamic equations for active matter from particle simulations and experiments.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Explainable AI: learning from the learners.Nature communications · 2026Review
- MultiCell: geometric learning in multicellular development.Nature methods · 2026Article
- Reconstructing Actin Dynamics of the Leading Edge from Observational Data.bioRxiv : the preprint server for biology · 2026Article
- Nonreciprocal field theory for decision-making in multi-agent control systems.Nature communications · 2025Article
- Nonlinear memory in cell-division dynamics across species.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Physics-tailored machine learning reveals unexpected physics in dusty plasmas.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Vortex reversal is a precursor of confined bacterial turbulence.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Learning collective cell migratory dynamics from a static snapshot with graph neural networks.ArXiv · 2024Article
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- Deep learning probability flows and entropy production rates in active matter.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Learning stochastic dynamics and predicting emergent behavior using transformers.Nature communications · 2024Article
- Article
- Review
- Learning mean-field equations from particle data using WSINDy.Physica D. Nonlinear phenomena · 2022Article
- Article
- Activity waves and freestanding vortices in populations of subcritical Quincke rollers.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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6 authors.
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Abstract
Recent advances in high-resolution imaging techniques and particle-based simulation methods have enabled the precise microscopic characterization of collective dynamics in various biological and engineered active matter systems. In parallel, data-driven algorithms for learning interpretable continuum models have shown promising potential for the recovery of underlying partial differential equations (PDEs) from continuum simulation data. By contrast, learning macroscopic hydrodynamic equations for active matter directly from experiments or particle simulations remains a major challenge, especially when continuum models are not known a priori or analytic coarse graining fails, as often is the case for nondilute and heterogeneous systems. Here, we present a framework that leverages spectral basis representations and sparse regression algorithms to discover PDE models from microscopic simulation and experimental data, while incorporating the relevant physical symmetries. We illustrate the practical potential through a range of applications, from a chiral active particle model mimicking nonidentical swimming cells to recent microroller experiments and schooling fish. In all these cases, our scheme learns hydrodynamic equations that reproduce the self-organized collective dynamics observed in the simulations and experiments. This inference framework makes it possible to measure a large number of hydrodynamic parameters in parallel and directly from video data.
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