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Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms.
A new look at the statistical model identification
H. Akaike · 1974
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Flocks, herds and schools: A distributed behavioral model
Craig W Reynolds · 1987
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Flocks, herds, and schools: A quantitative theory of flocking
John Toner and Yuhai Tu · 1998
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Relationship between cell migration and cell cycle during the initiation of epithelial to fibroblastoid transition
C Bonneton, J-B Sibarita, and J-P Thiery · 1999
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A non-local model for a swarm
Alexander Mogilner and Leah Edelstein-Keshet · 1999
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Self-organization in systems of self-propelled particles
Herbert Levine, Wouter-Jan Rappel, and Inon Cohen · 2000
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Cell cycle and cell migration: new pieces to the puzzle, 2001
Manfred Boehm and Elizabeth G Nabel · 2001
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Swarming patterns in a two-dimensional kinematic model for biological groups
Chad M Topaz and Andrea L Bertozzi · 2004
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Modelling directional guidance and motility regulation in cell migration
Anna Q Cai, Kerry A Landman, and Barry D Hughes · 2006
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Group decision making in honey bee swarms: When 10,000 bees go house hunting, how do they cooperatively choose their new nesting site?
Thomas D Seeley, P Kirk Visscher, and Kevin M Passino · 2006
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Emergent behavior in flocks
Felipe Cucker and Steve Smale · 2007
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Complex spatial group patterns result from different animal communication mechanisms
Raluca Eftimie, Gerda de Vries, and Mark A Lewis · 2007
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Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study
M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic · 2008
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Particle, kinetic, and hydrodynamic models of swarming
José A Carrillo, Massimo Fornasier, Giuseppe Toscani, and Francesco Vecil · 2010
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Heterogeneous particle swarm optimization
Andries P Engelbrecht · 2010
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Determining interaction rules in animal swarms
Anders Eriksson, Martin Nilsson Jacobi, Johan Nyström, and Kolbjørn Tunstrøm · 2010
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Inferring individual rules from collective behavior
Ryan Lukeman, Yue-Xian Li, and Leah Edelstein-Keshet · 2010
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The mechanics and statistics of active matter
Sriram Ramaswamy · 2010
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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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Inferring the structure and dynamics of interactions in schooling fish
Yael Katz, Kolbjørn Tunstrøm, Christos C Ioannou, Cristián Huepe, and Iain D Couzin · 2011
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Collective cell guidance by cooperative intercellular forces
Dhananjay T Tambe, C Corey Hardin, Thomas E Angelini, Kavitha Rajendran, Chan Young Park, Xavier Serra-Picamal, Enhua H Zhou, Muhammad H Zaman, James P Butler, David A Weitz, et al · 2011
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Understanding cancer stem cell heterogeneity and plasticity
Dean G Tang · 2012
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Collective motion
Tamás Vicsek and Anna Zafeiris · 2012
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Collective States, Multistability and Transitional Behavior in Schooling Fish
Kolbjørn Tunstrøm, Yael Katz, Christos C. Ioannou, Cristián Huepe, Matthew J. Lutz, and Iain D. Couzin · 2013
Cited alongside, same era.
Methods for numerical differentiation of noisy data
Ian Knowles and Robert J Renka · 2014
Cited alongside, same era.
Anisotropic interactions in a first-order aggregation model
Joep HM Evers, Razvan C Fetecau, and Lenya Ryzhik · 2015
Cited alongside, same era.
Collective cell migration: Guidance principles and hierarchies
Anna Haeger, Katarina Wolf, Mirjam M. Zegers, and Peter Friedl · 2015
Cited alongside, same era.
Potential of heterogeneity in collective behaviors: A case study on heterogeneous swarms
Daniela Kengyel, Heiko Hamann, Payam Zahadat, Gerald Radspieler, Franz Wotawa, and Thomas Schmickl · 2015
Cited alongside, same era.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Inferring the dynamics of underdamped stochastic systems
David B Brückner, Pierre Ronceray, and Chase P Broedersz · 2020
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A data-driven method for reconstructing and modelling social interactions in moving animal groups
R Escobedo, V Lecheval, V Papaspyros, F Bonnet, F Mondada, Clément Sire, and Guy Theraulaz · 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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Examination of an averaging method for estimating repulsion and attraction interactions in moving groups
Rajnesh K Mudaliar and Timothy M Schaerf · 2020
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Decoding collective communications using information theory tools
KR Pilkiewicz, BH Lemasson, MA Rowland, A Hein, J Sun, A Berdahl, ML Mayo, J Moehlis, M Porfiri, E Fernández-Juricic, et al · 2020
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Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Cited alongside, same era.
Modeling keratinocyte wound healing dynamics: Cell–cell adhesion promotes sustained collective migration
John T Nardini, Douglas A Chapnick, Xuedong Liu, and David M Bortz · 2016
Cited alongside, same era.
Cellular contraction and polarization drive collective cellular motion
Jacob Notbohm, Shiladitya Banerjee, Kazage JC Utuje, Bomi Gweon, Hwanseok Jang, Yongdoo Park, Jennifer Shin, James P Butler, Jeffrey J Fredberg, and M Cristina Marchetti · 2016
Cited alongside, same era.
Anisotropic interaction rules in circular motions of pigeon flocks: an empirical study based on sparse bayesian learning
Duxin Chen, Bowen Xu, Tao Zhu, Tao Zhou, and Hai-Tao Zhang · 2017
Cited alongside, same era.
A mathematical model coupling polarity signaling to cell adhesion explains diverse cell migration patterns
William R Holmes, JinSeok Park, Andre Levchenko, and Leah Edelstein-Keshet · 2017
Cited alongside, same era.
Data-driven discovery of partial differential equations
Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
Cited alongside, same era.
Learning partial differential equations via data discovery and sparse optimization
Hayden Schaeffer · 2017
Cited alongside, same era.
Ai feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark · 2020
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Numerical differentiation of noisy data: A unifying multi-objective optimization framework
Floris Van Breugel, J Nathan Kutz, and Bingni W Brunton · 2020
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Data-driven discovery of emergent behaviors in collective dynamics
Ming Zhong, Jason Miller, and Mauro Maggioni · 2020
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Leadership Through Influence: What Mechanisms Allow Leaders to Steer a Swarm?
Sara Bernardi, Raluca Eftimie, and Kevin J. Painter · 2021
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Learning the dynamics of cell–cell interactions in confined cell migration
David B Brückner, Nicolas Arlt, Alexandra Fink, Pierre Ronceray, Joachim O Rädler, and Chase P Broedersz · 2021
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Mean-field limits: from particle descriptions to macroscopic equations
José A Carrillo and Young-Pil Choi · 2021
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Data-driven discovery of interacting particle systems using gaussian processes
Jinchao Feng, Yunxiang Ren, and Sui Tang · 2021
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Decomposition of cell activities revealing the role of the cell cycle in driving biofunctional heterogeneity
Tian Lan, Meng Yu, Weisheng Chen, Jun Yin, Hsiang-Tsun Chang, Shan Tang, Ye Zhao, Spyros Svoronos, Samuel WK Wong, and Yiider Tseng · 2021
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Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, and Sui Tang · 2021
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Learning mean-field equations from particle data using wsindy
Daniel A Messenger and David M Bortz · 2021
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Weak SINDy for partial differential equations
Daniel A Messenger and David M Bortz · 2021
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Weak SINDy: Galerkin-based data-driven model selection
Daniel A Messenger and David M Bortz · 2021
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Learning differential equation models from stochastic agent-based model simulations
John T Nardini, Ruth E Baker, Matthew J Simpson, and Kevin B Flores · 2021
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A minimal model for structure, dynamics, and tension of monolayered cell colonies
Debarati Sarkar, Gerhard Gompper, and Jens Elgeti · 2021
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A statistical method for identifying different rules of interaction between individuals in moving animal groups
T. M. Schaerf, J. E. Herbert-Read, and A. J. W. Ward · 2021
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Learning hydrodynamic equations for active matter from particle simulations and experiments
Rohit Supekar, Boya Song, Alasdair Hastewell, Alexander Mietke, and Jörn Dunkel · 2021
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Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
U. Fasel, J. N. Kutz, B. W. Brunton, and S. L. Brunton · 2022
Closest in time.
Online Weak-form Sparse Identification of Partial Differential Equations
Daniel A. Messenger, Emiliano Dall’Anese, and David M. Bortz · 2022
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