A brief study conducted as a part of major "Applied mathematics and computer science" at the HSE University.

Using Neural ODEs to fit a networked UIV model to an SIR model of an epidemic.

Thesis repository on neural ordinary differential equations used for coarse-graining molecular dynamics

SIGIR'2024: ReCODE: Modeling Repeat Consumption with Neural ODE

First two problems of my Master's Thesis on Coarse-Graining of Molecular Dynamics with Neural Ordinary Differential Equations

Putting [A]NODEs ([Augmented] Neural ODEs) to work, for Time Series prediction and simulation

neural ordinary differential equations for undergraduate students

I created a Graph Neural Ordinary Differential Equation (GNODE) for modeling Newtonian gravity.

Parallel-in-time integration of Neural ODEs with reduced basis approximation

Approximately Bayesian Ensembling for Parameterized Neural ODEs.

Experimenting with Neural Ordinary Differential Equations (NODE)

Reimplementations of some normalizing flow algorithms using tensorflow 2.1 and tensorflow probability 0.9

Kinetics-Constrained Neural Ordinary Differential Equations (KCNODE) that can be trained even with small data

Code accompanying paper "Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits" at L4DC 2022.

Graph neural ordinary differential equations

Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs

Code and figures for "Neural network differential equations for ion channel modelling".

Fit time-series data with a Neural Differential Equation!

Neural Ordinary Differential Equations for Inverse Kinematics

This study introduces the innovative Neural Ordinary Differential Equations (NODE) model tailored for stock price forecasting, utilizing continuous data interpretation for handling irregular time series efficiently. Empirical evidence showcases NODE's superiority, achieving over 76% reduction in MAE and RMSE metrics compared to baseline models, dem