Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.

Awesome-spatial-temporal-scientific-machine-learning-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included.

Epidemic spatial and temporal model setup and simulation. Focusing on state of the art epidemiology models such as universal differential equations, epidemiology-informed deep learning models. Part of Mathepia.jl

Physics-Informed LSTM: Deep learning architecture incorporating stochastic car-following structure and LSTM for predicting human driving behavior (Universal Differential Equations)

Spatial and temporal epidemiology data mining flow tools including data processing and analysis, model setup and simulation, inference and evaluation. Focusing on state-of-the-art methods such as universal differential equations, epidemiology-informed deep learning methods.

Tutorials on math epidemiology and epidemiology informed deep learning methods

Solving Universal Differential Equations in Julia

Optimal epidemic control by deep learning techniques

Codes for Paper "Discovering first-principle behavior change transmission models by deep learning methods, Pengfei Song, Yanni Xiao, Jianhong Wu."

Codes for paper "Estimating time-varying reproduction number by deep learning techniques"

Physics-Informed Meta Graph Transformer for Travel State Estimation via Traffic Density

Trabajo de Fin de Grado de Ingeniería del Software: Diseño, implementación y validación de un modelo híbrido basado en el Modelo Mínimo de Bergman, con la finalidad de mejorar la capacidad de predicción incorporando una red neuronal para estimar el parámetro que determina la capacidad de la insulina para aumentar la captación de la glucosa.

Physics-informed and pure-mechanistic Universal Differential Equations (UDEs) for modeling tumor–immune dynamics. Dual Julia pipelines: Enhanced PI-NODE with residual/correction subnetworks for hybrid physics-neural learning, and Pure-Mechanistic UDE with biologically-constrained Gompertz growth and saturating immune-killing.