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• W3186331472 abstract "Large collections of coupled, heterogeneous agents can manifest complex dynamical behavior presenting difficulties for simulation and analysis. However, if the collective dynamics lie on a low-dimensional manifold, then the original agent-based model may be approximated with a simplified surrogate model on and near the low-dimensional space where the dynamics live. Analytically identifying such simplified models can be challenging or impossible, but here we present a data-driven coarse-graining methodology for discovering such reduced models. We consider two types of reduced models: globally based models that use global information and predict dynamics using information from the whole ensemble and locally based models that use local information, that is, information from just a subset of agents close (close in heterogeneity space, not physical space) to an agent, to predict the dynamics of an agent. For both approaches, we are able to learn laws governing the behavior of the reduced system on the low-dimensional manifold directly from time series of states from the agent-based system. These laws take the form of either a system of ordinary differential equations (ODEs), for the globally based approach, or a partial differential equation (PDE) in the locally based case. For each technique, we employ a specialized artificial neural network integrator that has been templated on an Euler time stepper (i.e., a ResNet) to learn the laws of the reduced model. As part of our methodology, we utilize the proper orthogonal decomposition (POD) to identify the low-dimensional space of the dynamics. Our globally based technique uses the resulting POD basis to define a set of coordinates for the agent states in this space and then seeks to learn the time evolution of these coordinates as a system of ODEs. For the locally based technique, we propose a methodology for learning a partial differential equation representation of the agents; the PDE law depends on the state variables and partial derivatives of the state variables with respect to model heterogeneities. We require that the state variables are smooth with respect to model heterogeneities, which permit us to cast the discrete agent-based problem as a continuous one in heterogeneity space. The agents in such a representation bear similarity to the discretization points used in typical finite element/volume methods. As an illustration of the efficacy of our techniques, we consider a simplified coupled neuron model for rhythmic oscillations in the pre-Bötzinger complex and demonstrate how our data-driven surrogate models are able to produce dynamics comparable to the dynamics of the full system. A nontrivial conclusion is that the dynamics can be equally well reproduced by an all-to-all coupled and by a locally coupled model of the same agents." @default.
• W3186331472 created "2021-08-02" @default.
• W3186331472 creator A5001340107 @default.
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• W3186331472 date "2021-07-01" @default.
• W3186331472 modified "2023-09-27" @default.
• W3186331472 title "Global and local reduced models for interacting, heterogeneous agents" @default.
• W3186331472 cites W1914428336 @default.
• W3186331472 cites W1940618937 @default.
• W3186331472 cites W1959895834 @default.
• W3186331472 cites W1969696351 @default.
• W3186331472 cites W1971123672 @default.
• W3186331472 cites W1972005403 @default.
• W3186331472 cites W1973243996 @default.
• W3186331472 cites W1976068300 @default.
• W3186331472 cites W1976485125 @default.
• W3186331472 cites W1978617162 @default.
• W3186331472 cites W1994089881 @default.
• W3186331472 cites W1996526323 @default.
• W3186331472 cites W2016447323 @default.
• W3186331472 cites W2017745440 @default.
• W3186331472 cites W2018259456 @default.
• W3186331472 cites W2029454358 @default.
• W3186331472 cites W2036425975 @default.
• W3186331472 cites W2037896057 @default.
• W3186331472 cites W2041178263 @default.
• W3186331472 cites W2057175858 @default.
• W3186331472 cites W2058580716 @default.
• W3186331472 cites W2059521275 @default.
• W3186331472 cites W2060923854 @default.
• W3186331472 cites W2065135854 @default.
• W3186331472 cites W2065297008 @default.
• W3186331472 cites W2071128523 @default.
• W3186331472 cites W2071836524 @default.
• W3186331472 cites W2072835751 @default.
• W3186331472 cites W2085049002 @default.
• W3186331472 cites W2088420423 @default.
• W3186331472 cites W2089143620 @default.
• W3186331472 cites W2091283133 @default.
• W3186331472 cites W2091772836 @default.
• W3186331472 cites W2097553780 @default.
• W3186331472 cites W2112823474 @default.
• W3186331472 cites W2116976114 @default.
• W3186331472 cites W2120615054 @default.
• W3186331472 cites W2130518506 @default.
• W3186331472 cites W2138484437 @default.
• W3186331472 cites W2140491322 @default.
• W3186331472 cites W2149723649 @default.
• W3186331472 cites W2294798173 @default.
• W3186331472 cites W2305107041 @default.
• W3186331472 cites W2525258218 @default.
• W3186331472 cites W2586637063 @default.
• W3186331472 cites W2631081867 @default.
• W3186331472 cites W2787296320 @default.
• W3186331472 cites W2790650423 @default.
• W3186331472 cites W2798188588 @default.
• W3186331472 cites W2810323699 @default.
• W3186331472 cites W2899283552 @default.
• W3186331472 cites W2901203181 @default.
• W3186331472 cites W2905558281 @default.
• W3186331472 cites W2949836709 @default.
• W3186331472 cites W2963339125 @default.
• W3186331472 cites W2963983868 @default.
• W3186331472 cites W2964085201 @default.
• W3186331472 cites W2986795381 @default.
• W3186331472 cites W2989632150 @default.
• W3186331472 cites W2999271947 @default.
• W3186331472 cites W3014009018 @default.
• W3186331472 cites W3024519426 @default.
• W3186331472 cites W3039119913 @default.
• W3186331472 cites W3042646207 @default.
• W3186331472 cites W3043699273 @default.
• W3186331472 cites W3096173239 @default.
• W3186331472 cites W3100100244 @default.
• W3186331472 cites W3100969891 @default.
• W3186331472 cites W3101571838 @default.
• W3186331472 cites W3102140816 @default.
• W3186331472 cites W3102551889 @default.
• W3186331472 cites W3103145119 @default.
• W3186331472 cites W3105521390 @default.
• W3186331472 cites W3105919389 @default.
• W3186331472 cites W3127476539 @default.
• W3186331472 cites W3136308753 @default.
• W3186331472 cites W3137098741 @default.
• W3186331472 cites W3178968719 @default.
• W3186331472 cites W4240485910 @default.
• W3186331472 doi "https://doi.org/10.1063/5.0055840" @default.
• W3186331472 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34340348" @default.
• W3186331472 hasPublicationYear "2021" @default.
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