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This is a quick introduction to neural ODEs for scientific applications. The goal is to (a) provide a modelling tool that enhances the expressivity of existing theory-driven approaches, (b) demonstrate that neural ODEs are easy to use via modern autodifferentiable software, and (c) give enough of the tips-and-tricks needed to make neural ODEs work in […] |
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Many studies adopt low-resolution, coarse-grained (CG) models to investigate polymers, proteins, and other soft materials. These studies must first specify the details that are retained in the low-resolution model, i.e., they must specify the “CG representation.” Unfortunately, the “best” representation for complex systems is not always obvious. In this study, we systematically explore the space […] |
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