Lavigne, Gary M, Jr

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VCU Scholars Compass

Masthead LogoVirginia Commonwealth UniversityVCU Scholars CompassBiology and Medicine Through MathematicsConference 2019May 15th, 4:30 PMNon-mechanistic Learning of PDEs from DiscreteSpatial Data in BiologyGary M. Lavigne JrNorth Carolina State University at Raleigh, gmlavign@ncsu.eduFollow this and additional works at: https://scholarscompass.vcu.edu/bammPart of the Life Sciences Commons, Medicine and Health Sciences Commons, and the PhysicalSciences and Mathematics CommonsThis Event is brought to you for free and open access by the Dept. of Mathematics and Applied Mathematics at VCU Scholars Compass. It has beenaccepted for inclusion in Biology and Medicine Through Mathematics Conference by an authorized administrator of VCU Scholars Compass. Formore information, please contact libcompass@vcu.edu.https://scholarscompass.vcu.edu/bamm/2019/wed/12Non-mechanistic Learning of PDEs from DiscreteSpatial Data in BiologyG. Michael LavigneMarch 1, 2019Stochastic models of biological phenomena are often favored over their con-tinuous ODE and PDE counterparts as they are able to incorporate more rele-vant biological phenomena and better represent the inherent randomness in realbiological systems. Differential Equation models, however, may be analyzed tomake inferences about both the transient and long-term behavior of the system.We will discuss an approach to non-mechanistically learn PDEs from discretedata that are generated from stochastic models. We will investigate the learningof PDEs both from stochastic processes that are classically known to upscale toPDEs such as biased random walks as well as more complex Cellular Automatamodels that are not known to have a continuum limit, with an eye towardslearning PDEs from noisy experimental data.1

Non-mechanistic Learning of PDEs from Discrete Spatial Data in Biology

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1332&context=bamm

Non-mechanistic Learning of PDEs from Discrete Spatial Data in Biology

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