20.500.12556/RUL-165157 Setting the standard for machine learning in phase field prediction a benchmark dataset and baseline metrics 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. machine learning neural network phase field model dataset strojno učenje nevronske mreže model faznega polja zbirka podatkov true false true Angleški jezik Slovenski jezik Članek v reviji 2024-11-25 11:21:57 2024-11-25 11:22:00 2025-04-04 10:22:48 0000-00-00 00:00:00 2024 0 0 10 str. Vol. 11, [art. no.] 1275 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85 2052-4463 10.1038/s41597-024-04128-9 216315651 Hannemose_..._Setting_the_standard_for_machine_learning_....pdf Hannemose_..._Setting_the_standard_for_machine_learning_....pdf 1 AD19CF6C62098507833C7A6C7F322B24 05426bab9c7192e8ed4819a41051d0aba7cefdb3146ad8ceb2ab2c4f598fc07e 10a850b7-ab17-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196139 https://www.nature.com/articles/s41597-024-04128-9 0 b3c3e705-ab16-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196138 Fakulteta za strojništvo 0 0 0