Physics-informed convolutional neural networks (PICNNs) have emerged as a powerful extension of physics-informed neural networks (PINNs), offering superior generalization and efficiency for solving ...

This research introduces an accelerated training approach for Vanilla Physics-Informed Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial weight state of the ...

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 ...