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 rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging from mechanistic models grounded in biophysics, to phenomenological descriptions of neural ...
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 ...
This repository is the community implementation version of this paper "HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs". This paper proposes a novel framework that treats ...
EVEN from the point of view of an undergraduate, the subject of differential equations is very diiferent from what it was fifty years ago. But in a large and miscellaneous collection of examples like ...
Abstract: The paper is devoted to the study of dynamic models of economic processes, which are described by systems of linear differential equations for the existence of a non-negative solution to the ...
An intermediate level course in the analytical and numerical study of ordinary differential equations, with an emphasis on their applications to the real world. Exact solution methods for ordinary ...
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