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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
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2024
Fag:
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
Online adgang:ONIX_20240704_9783725812813_123
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Summary: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.

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