دورية أكاديمية
Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty
| العنوان: | Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty |
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| المؤلفون: | Daniele Lanzoni, Marco Albani, Roberto Bergamaschini, Francesco Montalenti |
| المساهمون: | Lanzoni, D, Albani, M, Bergamaschini, R, Montalenti, F |
| بيانات النشر: | American Physical Society US |
| سنة النشر: | 2022 |
| المجموعة: | Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive) |
| مصطلحات موضوعية: | Machine Learning, Surface, Phase Field, Recurrent Neural Network, Convolutional Neural Network, Cahn-Hilliard, Prediction uncertainty |
| الوصف: | We use a convolutional, recurrent neural network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g., leading to the splitting of high aspect-ratio individual structures). The automatic smart augmentation of the training set and design of a hybrid simulation method are discussed. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | ELETTRONICO |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/wos/WOS:000866506400001; volume:6; issue:10; journal:PHYSICAL REVIEW MATERIALS; https://hdl.handle.net/10281/396131Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85140218796 |
| DOI: | 10.1103/PhysRevMaterials.6.103801 |
| الإتاحة: | https://doi.org/10.1103/PhysRevMaterials.6.103801Test https://hdl.handle.net/10281/396131Test |
| رقم الانضمام: | edsbas.BBDDD49E |
| قاعدة البيانات: | BASE |
| DOI: | 10.1103/PhysRevMaterials.6.103801 |
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