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This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems.
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Martin Feinberg and Friedrich JM Horn · 1974
Earlier work this paper cites.
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J Schnakenberg · 1979
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Ramiro Rico-Martinez and Ioannis G Kevrekidis · 1993
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Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin · 2013
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Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
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Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Tong Qin, Kailiang Wu, and Dongbin Xiu · 2019
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Tom Bertalan, Felix Dietrich, Igor Mezić, and Ioannis G Kevrekidis · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, and Paris Perdikaris · 2019
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Adversarial uncertainty quantification in physics-informed neural networks
Yibo Yang and Paris Perdikaris · 2019
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Alexis Tantet, Valerio Lucarini, Frank Lunkeit, and Henk A Dijkstra · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Catherine Brennan and Daniele Venturi · 2018
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2018
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Fast gaussian process based gradient matching for parameter identification in systems of nonlinear odes
Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, and Joachim M. Buhmann · 2019
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Data-driven discovery of coordinates and governing equations
Kathleen Champion, Bethany Lusch, J Nathan Kutz, and Steven L Brunton · 2019
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Composable effects for flexible and accelerated probabilistic programming in numpyro
Du Phan, Neeraj Pradhan, and Martin Jankowiak · 2019
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis · 2020
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Universal differential equations for scientific machine learning
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Physics-informed generative adversarial networks for stochastic differential equations
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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks
Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R Witschey, John A Detre, and Paris Perdikaris · 2020
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Covid-19 dynamics across the us: A deep learning study of human mobility and social behavior
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Bayesian differential programming for robust systems identification under uncertainty
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Bayesian neural ordinary differential equations
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Systems biology informed deep learning for inferring parameters and hidden dynamics
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Scalable gradients for stochastic differential equations
Xuechen Li, Ting-Kam Leonard Wong, Ricky TQ Chen, and David Duvenaud · 2020
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Learning dynamical systems from data: A simple cross-validation perspective, part i: Parametric kernel flows
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