Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics ...

Numerical analyses and surrogate models based on the compressible Euler and Navier–Stokes equations are essential for understanding and estimating nonlinear physical phenomena in fluid dynamics.

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 ...