Phase-field modeling offers a powerful tool for investigating the electrical control of the
domain structure in ferroelectrics. However, its broad application is constrained by …

The article provides a detailed review of the utilisation of machine learning (ML) in various
domains of additive manufacturing (AM) and highlights its potential to address key …

Computational thermo-microstructural modelling has become a powerful tool for
understanding the process-microstructure linkage in the Laser Powder Bed Fusion (PBF-LB) …

Metal additive manufacturing (AM) faces challenges in rapid selection and optimization of
manufacturing parameters for desired part quality. As a more efficient alternative to …

High-throughput phase-field simulations emerge as a compelling technique to predict the
evolution of domain structures in ferroelectric materials. Despite their potential, their …

Low computing efficiency is a significant barrier in the phase-field modeling of multi-
component alloys coupled with the CALPHAD (CALculation of PHAse Diagram) method …

Unlike classical artificial neural networks, which require retraining for each new set of
parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep …

Additive manufacturing by wire deposition is a complex process as it generates overlapping
and transient thermal fields, resulting in multiple cycles of solidification and remelting …

The difference of cooling rates on the surface and the interior of thin-walled structures leads
to significant differences of microstructures in additive manufacturing (AM). To reveal the …

We present a comprehensive benchmarking framework for evaluating machine-learning
approaches applied to phase-field problems. This framework focuses on four key analysis …