Applied Mathematics and Machine Learning
The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; o...
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| Format: | Online |
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| Language: | English |
| Published: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | ONIX_20240704_9783725812813_123 |
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| _version_ | 1869524728634408960 |
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| collection | Directory of Open Access Books |
| description | The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; on the other hand, theoretical discoveries in mathematical algorithms, differential equations, and statistical inferences, to name a few, have provided the foundation for the exploration of new multidisciplinary models for solving practical problems. This Special Issue endeavors to continue the journey that started in our previous Special Issue (Applied Mathematics and Computational Physics) by providing a platform for researchers from both academia and industry, as well as government, to present their new computational methods that have engineering and physics applications. We publish papers from all areas of mathematics and engineering, and especially those that showcase novel machine learning techniques that leverage subject matter expertise. We aim to foster the communication of the latest research results in the areas of applied and computational mathematics. |
| format | Online |
| id | doab-20.500.12854ir-139327 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1393272024-07-04T09:42:35Z Applied Mathematics and Machine Learning Li, Qun Wood, Aihua self-compacting concrete compressive strength deep neural network gradient boosting machine machine learning Dbar-dressing method Cauchy matrix Lax pair soliton solutions entropy fuzzy TOPSIS multi-criteria decision making financial ratio ranking data envelopment analysis efficiency operational risk potential improvement Korteweg–de Vries equation coarse grid digital twin Industry 4.0 supply chain bibliometric analysis subject area false information detection residual structure graph neural network electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) deep learning neural networks Gallium Arsenide (GaAs) SAR-X Casetti’s model climate variables prediction RShiny dynamical systems autoencoders latent representation manifold learning thema EDItEUR::P Mathematics and Science thema EDItEUR::P Mathematics and Science::PB Mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; on the other hand, theoretical discoveries in mathematical algorithms, differential equations, and statistical inferences, to name a few, have provided the foundation for the exploration of new multidisciplinary models for solving practical problems. This Special Issue endeavors to continue the journey that started in our previous Special Issue (Applied Mathematics and Computational Physics) by providing a platform for researchers from both academia and industry, as well as government, to present their new computational methods that have engineering and physics applications. We publish papers from all areas of mathematics and engineering, and especially those that showcase novel machine learning techniques that leverage subject matter expertise. We aim to foster the communication of the latest research results in the areas of applied and computational mathematics. 2024-07-04T09:42:32Z 2024-07-04T09:42:32Z 2024 book ONIX_20240704_9783725812813_123 9783725812813 9783725812820 https://directory.doabooks.org/handle/20.500.12854/139327 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9324 https://mdpi.com/books/pdfview/book/9324 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1282-0 10.3390/books978-3-7258-1282-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725812813 9783725812820 170 open access |
| spellingShingle | self-compacting concrete compressive strength deep neural network gradient boosting machine machine learning Dbar-dressing method Cauchy matrix Lax pair soliton solutions entropy fuzzy TOPSIS multi-criteria decision making financial ratio ranking data envelopment analysis efficiency operational risk potential improvement Korteweg–de Vries equation coarse grid digital twin Industry 4.0 supply chain bibliometric analysis subject area false information detection residual structure graph neural network electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) deep learning neural networks Gallium Arsenide (GaAs) SAR-X Casetti’s model climate variables prediction RShiny dynamical systems autoencoders latent representation manifold learning thema EDItEUR::P Mathematics and Science thema EDItEUR::P Mathematics and Science::PB Mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics Applied Mathematics and Machine Learning |
| title | Applied Mathematics and Machine Learning |
| title_full | Applied Mathematics and Machine Learning |
| title_fullStr | Applied Mathematics and Machine Learning |
| title_full_unstemmed | Applied Mathematics and Machine Learning |
| title_short | Applied Mathematics and Machine Learning |
| title_sort | applied mathematics and machine learning |
| topic | self-compacting concrete compressive strength deep neural network gradient boosting machine machine learning Dbar-dressing method Cauchy matrix Lax pair soliton solutions entropy fuzzy TOPSIS multi-criteria decision making financial ratio ranking data envelopment analysis efficiency operational risk potential improvement Korteweg–de Vries equation coarse grid digital twin Industry 4.0 supply chain bibliometric analysis subject area false information detection residual structure graph neural network electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) deep learning neural networks Gallium Arsenide (GaAs) SAR-X Casetti’s model climate variables prediction RShiny dynamical systems autoencoders latent representation manifold learning thema EDItEUR::P Mathematics and Science thema EDItEUR::P Mathematics and Science::PB Mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| topic_facet | self-compacting concrete compressive strength deep neural network gradient boosting machine machine learning Dbar-dressing method Cauchy matrix Lax pair soliton solutions entropy fuzzy TOPSIS multi-criteria decision making financial ratio ranking data envelopment analysis efficiency operational risk potential improvement Korteweg–de Vries equation coarse grid digital twin Industry 4.0 supply chain bibliometric analysis subject area false information detection residual structure graph neural network electron microscope convolutional neural networks (CNNs) anomaly detection principal component analysis (PCA) deep learning neural networks Gallium Arsenide (GaAs) SAR-X Casetti’s model climate variables prediction RShiny dynamical systems autoencoders latent representation manifold learning thema EDItEUR::P Mathematics and Science thema EDItEUR::P Mathematics and Science::PB Mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| url | ONIX_20240704_9783725812813_123 |