Scientific Machine Learning for Polymeric Materials
Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers’ distinct features at different structural and temporal scales (i.e., a subtle change in their...
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| Формат: | Online |
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| Язык: | английский |
| Опубликовано: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Предметы: | |
| Online-ссылка: | ONIX_20260416T142754_9783725858392_48 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869526167786094592 |
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| collection | Directory of Open Access Books |
| description | Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers’ distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML—e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators—for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.). |
| format | Online |
| id | doab-20.500.12854ir-174993 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1749932026-04-16T18:05:11Z Scientific Machine Learning for Polymeric Materials Faroughi, Salah A. Pinto Fernandes, Célio Scientific machine learning Physics-based deep learning Physics-guided neural networks Physics-informed neural networks Polymer simulation Viscoelastic fluids thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers’ distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML—e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators—for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.). 2026-04-16T18:05:04Z 2026-04-16T18:05:04Z 2025 book ONIX_20260416T142754_9783725858392_48 9783725858392 9783725858408 https://directory.doabooks.org/handle/20.500.12854/174993 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11892 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5840-8 10.3390/books978-3-7258-5840-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725858392 9783725858408 254 CH open access |
| spellingShingle | Scientific machine learning Physics-based deep learning Physics-guided neural networks Physics-informed neural networks Polymer simulation Viscoelastic fluids thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Scientific Machine Learning for Polymeric Materials |
| title | Scientific Machine Learning for Polymeric Materials |
| title_full | Scientific Machine Learning for Polymeric Materials |
| title_fullStr | Scientific Machine Learning for Polymeric Materials |
| title_full_unstemmed | Scientific Machine Learning for Polymeric Materials |
| title_short | Scientific Machine Learning for Polymeric Materials |
| title_sort | scientific machine learning for polymeric materials |
| topic | Scientific machine learning Physics-based deep learning Physics-guided neural networks Physics-informed neural networks Polymer simulation Viscoelastic fluids thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Scientific machine learning Physics-based deep learning Physics-guided neural networks Physics-informed neural networks Polymer simulation Viscoelastic fluids thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20260416T142754_9783725858392_48 |