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- W3100457622 abstract "Spatio-temporal systems exhibiting multi-scale behaviour are common in applications ranging from cyber-physical systems to systems biology, yet they present formidable challenges for computational modelling and analysis. Here we consider a prototypic scenario where spatially distributed agents decide their movement based on external inputs and a fast-equilibrating internal computation. We propose a generally applicable strategy based on statistically abstracting the internal system using Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning. We show on a running example of bacterial chemotaxis that this approach leads to accurate and much faster simulations in a variety of scenarios." @default.
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- W3100457622 date "2017-01-01" @default.
- W3100457622 modified "2023-09-27" @default.
- W3100457622 title "Statistical Abstraction for Multi-scale Spatio-Temporal Systems" @default.
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- W3100457622 doi "https://doi.org/10.1007/978-3-319-66335-7_15" @default.
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