Automated adaptive inference of phenomenological dynamical models
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DOI: 10.1038/ncomms9133
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Cited by:
- Aguilar-Canto, Fernando Javier & Brito-Loeza, Carlos & Calvo, Hiram, 2024. "Model discovery of compartmental models with Graph-Supported Neural Networks," Applied Mathematics and Computation, Elsevier, vol. 464(C).
- Wei, Baolei, 2022. "Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
- Fernández de la Mata, Félix & Gijón, Alfonso & Molina-Solana, Miguel & Gómez-Romero, Juan, 2023. "Physics-informed neural networks for data-driven simulation: Advantages, limitations, and opportunities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
- Zhao Chen & Yang Liu & Hao Sun, 2021. "Physics-informed learning of governing equations from scarce data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
- Charles D. Brummitt & Andres Gomez-Lievano & Ricardo Hausmann & Matthew H. Bonds, 2018. "Machine-learned patterns suggest that diversification drives economic development," Papers 1812.03534, arXiv.org.
- Mikhail Genkin & Owen Hughes & Tatiana A. Engel, 2021. "Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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