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We develop a weak-form sparse identification method for interacting particle systems (IPS) with the primary goals of reducing computational complexity for large particle number $N$ and offering robustness to either intrinsic or extrinsic noise.
Model for chemotaxis
Evelyn F Keller and Lee A Segel · 1971
Earlier work this paper cites.
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David Freedman and Persi Diaconis · 1981
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Andrew W Lo · 1988
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Maximum likelihood theory for large interacting systems
Raphael A Kasonga · 1990
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Michael S Warren and John K Salmon · 1992
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Grigorii Noikhovich Milstein · 1994
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Continuous-time average-preserving opinion dynamics with opinion-dependent communications
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Estimation in interacting diffusions: Continuous and discrete sampling
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François Bolley, José A Canizo, and José A Carrillo · 2011
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Swarm dynamics and equilibria for a nonlocal aggregation model
Razvan C Fetecau, Yanghong Huang, and Theodore Kolokolnikov · 2011
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Principles of multiscale modeling
E Weinan · 2011
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Collective cell motion in an epithelial sheet can be quantitatively described by a stochastic interacting particle model
Néstor Sepúlveda, Laurence Petitjean, Olivier Cochet, Erwan Grasland-Mongrain, Pascal Silberzan, and Vincent Hakim · 2013
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Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results
Paul Van Liedekerke, MM Palm, N Jagiella, and Dirk Drasdo · 2015
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Motility-driven glass and jamming transitions in biological tissues
Dapeng Bi, Xingbo Yang, M Cristina Marchetti, and M Lisa Manning · 2016
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On mean field limits for dynamical systems
Niklas Boers and Peter Pickl · 2016
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
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Tony Lelievre and Gabriel Stoltz · 2016
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Inferring interaction rules from observations of evolutive systems i: The variational approach
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Jun-Gi Jang and U Kang · 2020
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Learning partial differential equations for biological transport models from noisy spatio-temporal data
John H. Lagergren, John T. Nardini, G. Michael Lavigne, Erica M. Rutter, and Kevin B. Flores · 2020
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Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles
Quanjun Lang and Fei Lu · 2020
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Equilibria of an aggregation model with linear diffusion in domains with boundaries
Daniel A Messenger and Razvan C Fetecau · 2020
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Low-rank tucker approximation of a tensor from streaming data
Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, and Madeleine Udell · 2020
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Mattia Bongini, Massimo Fornasier, Markus Hansen, and Mauro Maggioni · 2017
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Swarm equilibria in domains with boundaries
Razvan C Fetecau and Mitchell Kovacic · 2017
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Mean field limit for stochastic particle systems
Pierre-Emmanuel Jabin and Zhenfu Wang · 2017
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Data-driven discovery of partial differential equations
Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
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Sparse learning of stochastic dynamical equations
Lorenzo Boninsegna, Feliks Nüske, and Cecilia Clementi · 2018
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Zero-diffusion limit for aggregation equations over bounded domains
Razvan C Fetecau, Hui Huang, Daniel Messenger, and Weiran Sun · 2018
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Low-rank tucker decomposition of large tensors using tensorsketch
Osman Asif Malik and Stephen Becker · 2018
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Nonlinear stochastic modelling with Langevin regression
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