Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost in computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the …