Setting the standard for machine learning in phase field prediction : a benchmark dataset and baseline metrics

Hannemose Rieger, Laura (avtor), Zelič, Klemen (avtor), Mele, Igor (avtor), Katrašnik, Tomaž (avtor), Bhowmik, Arghya (avtor)

Phase field models are an important mesoscale method that serves as a bridge between the atomic scale and the macroscale, used for modeling complex phenomena at the microstructure level. Machine learning can be employed to accelerate these simulations, enabling faster and more efficient analyses. However, the development of new machine learning algorithms depends on access to extensive datasets. This work introduces an accessible and well-documented dataset aimed at benchmarking new machine learning algorithms. We validate the dataset with a benchmark using U-Net regression, a widely used neural network architecture. Although direct comparisons are limited by the lack of existing benchmarks, our model’s error metrics are competitive with previous work and generalize across multiple domain sizes. This contribution provides a valuable resource for future efforts in machine learning model development for phase field simulations and demonstrates the potential of U-Net regression, highlighting the scope for novel method development in this area.

• Jezik: Angleški jezik
• Ključne besede: machine learning, neural network, phase field model, dataset
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: 10 str.
• Številčenje: Vol. 11, [art. no.] 1275
• PID: 20.500.12556/RUL-165157
• UDK: 004.85
• ISSN pri članku: 2052-4463
• DOI: 10.1038/s41597-024-04128-9
• COBISS.SI-ID: 216315651
• Datum objave v RUL: 25.11.2024

Gradivo je del revije

• Naslov: Scientific data
• Založnik: Nature Publishing Group
• ISSN: 2052-4463
• COBISS.SI-ID: 523393305

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, nevronske mreže, model faznega polja, zbirka podatkov