Frank Wendler

Multiscale modelling of ferroelectrics using a physics-informed neural network driven by molecular dynamics data: parameter identification and field reconstruction

A physics-informed neural network (PINN) framework is developed, in which the loss function consists of MD-data penalty and partial difference equation (PDE) constraints, which provides an effective methodology for multiscale bridging between atomistic and continuum descriptions for perovskite ferroelectric materials.