An interactive toolbox for standardizing, validating, simulating, reducing, and exploring detailed biophysical models that can be used to reveal how morpho-electric properties map to dendritic and ...
Abstract: Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order ...
Abstract: By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs) ...
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