ArticleNature communications2015
Automated adaptive inference of phenomenological dynamical models.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Article
- Inverse problems for dynamic patterns in coupled oscillator networks: when larger networks are simpler.Nature communications · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Physics-tailored machine learning reveals unexpected physics in dusty plasmas.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dynamical systems theory as an organizing principle for single-cell biology.NPJ systems biology and applications · 2025Review
- Neural ordinary differential equations with irregular and noisy data.Royal Society open science · 2023Article
- A dynamical model ofProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Identifying regulation with adversarial surrogates.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Streaming Variational Monte Carlo.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Information geometry for multiparameter models: new perspectives on the origin of simplicity.Reports on progress in physics. Physical Society (Great Britain) · 2022Review
- Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer.NPJ systems biology and applications · 2022Article
- Review
- Latent space of a small genetic network: Geometry of dynamics and information.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics.Science (New York, N.Y.) · 2022Article
- Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Physics-informed learning of governing equations from scarce data.Nature communications · 2021Article
- Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.Nature communications · 2021Article
- Cluster-based network modeling-From snapshots to complex dynamical systems.Science advances · 2021Article
- Inferring phenomenological models of first passage processes.PLoS computational biology · 2021Article
- Causal Geometry.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.
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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.