Kinetic equations and level-set approach for simulating solid-state microstructure evolutions at the mesoscopic scale: state of the art, limitations, and prospects
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Abstract (arXiv)
For over three decades, the front-capturing level-set method has demonstrated its prowess for the simulation, at the mesoscopic scale, of numerous mechanisms in the context of microstructure evolution occurring during complex thermomechanical paths. This review delves into the foundations of this numerical framework, charting its evolution concerning polycrystalline materials, examining its recent advancements, scrutinizing its current shortcomings, and exploring future possibilities. Special attention will be given to the context of hot metal forming processes. In this context, this article also aims to reintroduce, as simply as possible, the kinetic equations related to the grain boundary migration.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Aug 2023.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computationscited by 4
- 2025Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Modelscited by 0
- 2026Physics-Informed Attention Mechanism and Generalization Capability of Deep Learning-Based Grain Growth Evolution Predictioncited by 0
- 2026Physics-Informed Attention Mechanism and Generalization Capability of Deep Learning-Based Grain Growth Evolution Prediction15 Jun 2026
- 2025Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models7 Nov 2025
- 2025High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations8 May 2025