January 04, 2021 article Open access Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods npj Computational Materials DOI: 10.1038/s41524-020-00471-8 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 234 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access A data-driven strategy for phase field nucleation modeling 2024 npj Materials Degradation article Materials Science Machine Learning in Materials Science Open access 6 citations Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations 2022 Computational Materials Science article Physics and Astronomy Model Reduction and Neural Networks Open access 29 citations Machine-learning-based surrogate modeling of microstructure evolution using phase-field 2022 Computational Materials Science article Materials Science Machine Learning in Materials Science 62 citations 3 results References 0