Neural Ordinary Differential Equations Overview and Summary I try to implement the findings in the paper in this repo. Here's a summary of what I think is significant information. Neural Ordinary ...

Neural differential equations are a promising new member in the neural network family. They show the potential of differential equations for time-series data analysis. In this paper, the strength of ...

Neural Ordinary Differential Equations (NODEs) revolutionize the way we view residual networks as solvers for initial value problems (IVPs), with layer depth serving as the time step. In this study, ...

Stochastic Physics-Informed Neural Ordinary Differential Equations (SPINODE) Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems.

Chemical kinetics, thermodynamics, and equilibrium: Chemical equilibrium; Chemical thermodynamics (first and second law); and Chemical kinetics (zero and first order reactions ... Differentiation; ...

In the context of modeling a system governed by ordinary differential equations (ODEs), three different approaches are presented and described in this section. The first corresponds to a standard ...