Applied Mathematics and Machine Learning

Download Url(s)
https://mdpi.com/books/pdfview/book/9324Contributor(s)
Li, Qun (editor)
Wood, Aihua (editor)
Language
EnglishAbstract
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.
Keywords
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 learningWebshop link
https://mdpi.com/books/pdfview ...ISBN
9783725812813, 9783725812820Publisher website
www.mdpi.com/booksPublication date and place
2024Classification
Mathematics and Science
Mathematics
Applied mathematics