Fractional physics-informed neural network approach to inverse problems in epidemiological modeling

Physics-Informed Neural Networks (PINNs) for Parameter Estimation in the SIRS-D Epidemiological Model

Infectious diseases exhibit complex and rapidly evolving transmission dynamics, requiring modeling approaches that can accurately capture these mechanisms. The SIRS-D compartmental model provides a suitable framework, as it incorporates temporary immunity and disease-induced mortality within the epidemic process. Accur...

Boosted training strategy for physics-informed neural networks in modeling non-linear computer virus dynamics with vertical transmission

A Physics-Informed Neural Network based framework for an e-epidemic SI1I2R model of computer-virus spread that incorporates a possibly transmissible class, an amply transmissible class, and direct transmission, allowing nodes to be initially compromised without contact is developed.

Physics-Informed Neural Networks for Fractional SEIR Models with Power Mittag-Leffler Kernels

Dynamical Analysis and SDEPINN-Based Modeling of a Fractional-Order Stochastic SIS Epidemic Model

Empirical results based on monthly influenza data from Xinjiang show that the proposed framework can capture epidemic trends, seasonal peaks, and fitting uncertainty and provide a theoretically grounded and practically applicable approach for infectious disease modeling under memory effects and stochastic perturbations...

A Discrete Fractional-Order Model of Diabetes–Cardiovascular Dynamics with Reservoir Computing–Based Forecasting

A novel discrete fractional-order mathematical framework for modeling and forecasting the coupled progression of diabetes and cardiovascular complications, with emphasis on population dynamics in Saudi Arabia, offers valuable insights for public health decision-making and epidemic mitigation.

A Fractional Physics-Informed Neural Network with Ensemble Learning for Dengue Forecasting

The findings suggest that the combination of fractional-order memory with physics-informed learning constitutes an effective and interpretable model for dengue forecasting, providing a sound model for epidemic forecasting.