Physics-Informed Neural Anhysteresis Surrogate for Magneto-Elastic Vector Hysteresis in Device Simulations

In many applications, accurately capturing the magneto-mechanical coupling and dissipative effects at the material level is essential for realistic simulations. Embedding the simplified multiscale model (SMSM) inside an energy-based hysteresis framework yields high fidelity but is computationally intensive for 3-D finite element (FE) analyses. This article introduces NNSMSM, a physics-informed multi-task deep neural network that emulates the expensive SMSM operator. A hybrid Latin-hypercube (LH)/Sobol sampling strategy efficiently explores the magneto-mechanical loading space. The network is trained with a composite loss that simultaneously fits magnetization and magnetostrictive strain while enforcing reciprocity and positive definiteness of the susceptibility tensor. The traced TorchScript model is linked to the open-source FE software openCFS, replacing the SMSM inside the vector play model (VPM) hysteresis model with zero code changes. The benchmark of a permanent magnet synchronous machine (PMSM) device simulation shows a speed-up of wall clock time by a factor of 11 while preserving global accuracy of hysteresis losses.