Publications (6)
Deep Learning for Accelerated Long-Horizon Forecasting of Multicomponent Multiphase Microstructure Evolution in High-Entropy Alloys
Hamidreza Razavi, Nele Moelans
The paper introduces a surrogate model combining autoencoders, graph convolutional networks, and LSTM to rapidly predict long‑term microstructure evolution in multicomponent high‑e…
A computationally efficient and mechanically compatible multi-phase-field model applied to coherently stressed three-phase solids
Sourav Chatterjee, Daniel Schwen, Nele Moelans
Engineering alloys generally exhibit multi-phase microstructures. For simulating their microstructure evolution during solid-state phase transformation, CALPHAD-guided multi-phase-…
An efficient and quantitative phase-field model for elastically heterogeneous two-phase solids based on a partial rank-one homogenization scheme
Sourav Chatterjee, Daniel Schwen, Nele Moelans
This paper presents an efficient and quantitative phase-field model for elastically heterogeneous alloys that ensures the two mechanical compatibilities$\unicode{x2014}$static and…
Influence of surface energy anisotropy on nucleation and crystallographic texture of polycrystalline deposits
Martin Minar, Nele Moelans
This paper aims to elucidate the role of interface energy anisotropy in orientation selection during nucleation of new grains in a polycrystalline film growth. An assessment of (he…
Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
Hamidreza Razavi, Nele Moelans
This paper presents a physics-informed framework that integrates graph convolutional networks (GCN) with long short-term memory (LSTM) architecture to forecast microstructure evolu…
Benchmarking of different strategies to include anisotropy in a curvature-driven multi-phase-field model
Martin Minar, Nele Moelans
Two benchmark problems for quantitative assessment of anisotropic curvature driving force in phase field method were developed and introduced. Both benchmarks contained an anisotro…