Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials
<|reference_start|>Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials: A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the ground truth ones obtained via conventional integration schemes. The method can closely reproduce the evolution of microstructures not represented in the training set at a fraction of the computational costs. Extremely long-time extrapolation capabilities are achieved, up to reaching the theoretically expected equilibrium state of the system, despite the training set containing only relatively-short, initial phases of the evolution. Quantitative accordance with the decay rate of the Free energy is also demonstrated up to late coarsening stages, providing an example of a data-driven, physically consistent and high-accuracy Machine Learning method for the long timescale simulation of materials.<|reference_end|>
@article{lanzoni2024extreme,
    title={Extreme time extrapolation capabilities and thermodynamic consistency of
  physics-inspired Neural Networks for the 3D microstructure evolution of
  materials via Cahn-Hilliard flow},
    author={Daniele Lanzoni, Andrea Fantasia, Roberto Bergamaschini, Olivier
  Pierre-Louis, Francesco Montalenti},
    journal={arXiv preprint arXiv:2407.20126},
    year={2024},
    doi={10.1088/2632-2153/ad8532},
    archivePrefix={arXiv},
    eprint={2407.20126},
    primaryClass={cond-mat.mes-hall cond-mat.mtrl-sci cs.LG physics.comp-ph}
}