Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Engineering and research communities are rapidly integrating AI into control system design, merging physics-based modeling, data-driven algorithms, and productivity tools to create faster, more ...