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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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Open access Aug 2026

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.

Jamshaid Ul Rahman, Shanza Shabeer, Noreen Mustafa et al. · 0 citations
Open access Jul 2026

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...

Fitri Cahyani, Abdurakhman Abdurakhman, Chyntia Meininda Anjanni · 0 citations
Jul 2026

Multi-scale physics-informed neural networks with Fourier features for approximating time-fractional PDEs

Extensive numerical experiments demonstrate that PINNs-MSFF achieves superior accuracy, stability, and convergence, effectively capturing complex fractional dynamics, sharp localized gradients, and dispersive phase transitions where standard PINNs often fail.

Harender Kumar · 0 citations
Aug 2026

SUC–PINNs: A physics-informed neural networks approach to inverse problems in epidemic models with partial observability

Experiments with synthetic data and COVID-19 surveillance data show that SUC–PINN recovers plausible hidden infection trajectories, yields stable parameter estimates, and provides accurate short-term forecasts, support SUC–PINN as a practical computational approach for inverse modeling and prediction in partially obser...

U. M. Rifanti, N. Susyanto, Ratinan Boonklurb · 0 citations
Open access Jul 2026

Fractional Physics Informed Neural Networks for Surrogate Modeling of Non-Markovian Discrete-Time Quantum Walks

FracPINN is introduced, a fractional physics-informed neural network that embeds a fully differentiable, PyTorch-based Caputo PDE solver into a classical LSTM encoder, and acts as a compact physics regularizer that constrains the inference with emergent fractional transport dynamics.

Zhaoyu Zhu, Ming-Xin Liu, Chengtian Liang et al. · 0 citations

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