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Center for Nonlinear Studies |
Progress in modeling complex, heterogeneous, spatially distributed, multiscale biological systems is being made by applying both machine learning methods (numerical AI - Artificial Intelligence) and declarative mathematical modeling languages including computer algebra (symbolic AI). I will use as examples reaction/diffusion networks relevant to modeling synapses during learning, and current work on cell-scale microtubule networks in plant development. Technical tools include “dynamic Boltzmann distributions” for model reduction by machine learning, and “dynamical graph grammars” for modeling evolving, spatially embedded structures. I will then attempt to set the stage for further discussions on AI for Computational Science by considering formal representations of computable scientific models and their stackable, conditionally valid reduction mappings, within a conceptual architecture for accumulating and applying computer-represented knowledge of the relevant applied mathematics.**This seminar is part of a series on Artificial Intelligence for Computational Science.
Host: Aric Hagberg