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Phase field data

dataset
posted on 2024-06-24, 06:34 authored by Laura RiegerLaura Rieger, Klemen Zelič, Igor Mele, Tomaž Katrašnik, Arghya BhowmikArghya Bhowmik

This dataset comprises tailored phase field prediction data generated using an innovative automated workflow designed to offer insights into complex phenomena while minimizing computational expenses. The dataset aims to facilitate benchmarking of new algorithms in phase field prediction, emphasizing accessibility and utility for researchers. The data creation process is detailed, focusing on streamlining data collection and preparation. Validation of the dataset's effectiveness is conducted through a benchmark experiment utilizing U-Net regression, a widely adopted neural network architecture. Results showcase competitive performance of the U-Net model, akin to previous state-of-the-art methods. This dataset not only serves as a valuable resource for the phase field prediction community but also highlights the potential of U-Net regression, fostering further advancements in the field.

The linked code can be found under https://github.com/laura-rieger/phasefield_benchmark and describes in detail how the dataset is to be used.


Funding

Battery Interface Genome - Materials Acceleration Platform

European Commission

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BATTERY 2030+ CSA3 large-scale research initiative: At the heart of a connected green society

European Commission

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