Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver.
Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters.
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.
Networking and Internet Architecture