2024
DOI: 10.1088/1361-651x/ad5f4a
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Benchmarking machine learning strategies for phase-field problems

Rémi Dingreville,
Andreas E Roberston,
Vahid Attari
et al.

Abstract: We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between … Show more

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