Machine Learning in Modeling and Simulation [E-Book] : Methods and Applications / edited by Timon Rabczuk, Klaus-Jürgen Bathe.
Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Scienc...
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Full text |
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| Personal Name(s): | Bathe, Klaus-Jürgen, editor |
| Rabczuk, Timon, editor | |
| Edition: |
1st edition 2023. |
| Imprint: |
Cham :
Springer,
2023
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| Physical Description: |
IX, 451 pages 150 illustrations, 135 illustrations in color (online resource) |
| Note: |
englisch |
| ISBN: |
9783031366444 |
| DOI: |
10.1007/978-3-031-36644-4 |
| Series Title: |
Computational Methods in Engineering & the Sciences
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| Subject (ZB): | |
| Classification: |
- Machine Learning in Computer-Aided Engineering
- Artificial Neural Networks
- Gaussian Processes
- Machine Learning Methods for Constructing Dynamic Models from Data
- Physics-Informed Neural Networks: Theory and Applications
- Physics-Informed Deep Neural Operator Networks
- Digital Twin for Dynamical Systems
- Reduced Order Modeling
- Regression Models for Machine Learning
- Overview on Machine Learning Assisted Topology Optimization Methodologies
- Mixed-variable Concurrent Material, Geometry and Process Design in Integrated Computational Materials Engineering
- Machine Learning Interatomic Potentials: Keys to First-principles Multiscale Modeling.