Physics-Informed Neural Networks (PINNs) augment traditional neural architectures by embedding the governing equations of physical systems directly into the loss function. Instead of solely minimising ...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). But they often stumble when ...

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 ...

A case study in aerospace manufacturing provides an overview of how physics-informed digital twin systems transform robotics processes—from adaptive process planning and real-time process monitoring ...