Machine Learning in Modeling and Simulation : Methods and Applications 9783031366444, 9783031366437
Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, me
109 24 74MB
English Pages 802 Year 2023
Table of contents :
Cover
Front Matter
1. Machine Learning in Computer Aided Engineering
2. Artificial Neural Networks
3. Gaussian Processes
4. Machine Learning Methods for Constructing Dynamic Models From Data
5. Physics-Informed Neural Networks: Theory and Applications
6. Physics-Informed Deep Neural Operator Networks
7. Digital Twin for Dynamical Systems
8. Reduced Order Modeling
9. Regression Models for Machine Learning
10. Overview on Machine Learning Assisted Topology Optimization Methodologies
11. Mixed-Variable Concurrent Material, Geometry, and Process Design in Integrated Computational Materials Engineering
12. Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
Cover
Front Matter
1. Machine Learning in Computer Aided Engineering
2. Artificial Neural Networks
3. Gaussian Processes
4. Machine Learning Methods for Constructing Dynamic Models From Data
5. Physics-Informed Neural Networks: Theory and Applications
6. Physics-Informed Deep Neural Operator Networks
7. Digital Twin for Dynamical Systems
8. Reduced Order Modeling
9. Regression Models for Machine Learning
10. Overview on Machine Learning Assisted Topology Optimization Methodologies
11. Mixed-Variable Concurrent Material, Geometry, and Process Design in Integrated Computational Materials Engineering
12. Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
- Author / Uploaded
- Timon Rabczuk
- Klaus-Jürgen Bathe
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