The rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging from mechanistic models grounded in biophysics, to phenomenological descriptions of neural ...
Metabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale ...
Abstract: The article considers models of neural systems in the form of functional differential equations, models with distributed delay, obtained by generalizing their corresponding systems with ...
Metabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale ...
Physics-Informed Neural Networks (PINNs) are a class of deep learning models designed to solve differential equations by incorporating physical laws directly into the training process. Instead of ...
The study of chemotaxis, the directed movement of cells or organisms in response to chemical gradients, is fundamentally linked to the development and analysis of partial differential equations (PDEs) ...
A long-standing problem at the interface of artificial intelligence and applied mathematics is to devise an algorithm capable of achieving human level or even superhuman proficiency in transforming ...
Partial differential equations (PDEs) lie at the heart of many different fields of Mathematics and Physics: Complex Analysis, Minimal Surfaces, Kähler and Einstein Geometry, Geometric Flows, ...
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