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 ...
More information Zitong Ye et al, Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network, Intelligent Computing ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results