ArticleProceedings of the National Academy of Sciences of the United States of America2021
Machine learning active-nematic hydrodynamics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Topological control of spontaneous failure in active nematic solids.Nature materials · 2026Article
- Mixing of a binary passive particle system using smart active particles.Scientific reports · 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
- The 2025 motile active matter roadmap.Journal of physics. Condensed matter : an Institute of Physics journal · 2025Review
- A hitchhiker's guide to active motion.The European physical journal. E, Soft matter · 2025Article
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.eLife · 2024Article
- Motor crosslinking augments elasticity in active nematics.Soft matter · 2024Article
- Machine learning topological defects in confluent tissues.Biophysical reports · 2024Article
- Harnessing synthetic active particles for physical reservoir computing.Nature communications · 2024Article
- Kinesin and myosin motors compete to drive rich multiphase dynamics in programmable cytoskeletal composites.PNAS nexus · 2023Article
- Physically informed data-driven modeling of active nematics.Science advances · 2023Article
- Neural networks determination of material elastic constants and structures in nematic complex fluids.Scientific reports · 2023Article
- Learning hydrodynamic equations for active matter from particle simulations and experiments.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Competing instabilities reveal how to rationally design and control active crosslinked gels.Nature communications · 2022Article
- Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics.Journal of visualized experiments : JoVE · 2022Article
- Article
- Learning active nematics one step at a time.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.
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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.