Machine Learning in Modeling and Simulation
This course serves as a comprehensive introduction to the application of Machine Learning (ML) techniques in modeling and simulation, with a focus on engineering and scientific problems. It covers the fundamental principles of ML and progresses to advanced topics, including physics-informed machine learning, reduced-order modeling, digital twins, and the use of ML for material and structural design. The course will strike a balance between understanding the mathematical and theoretical underpinnings of the methods and exploring their practical applications. Students will gain essential knowledge for conducting research and solving problems in computational science, engineering, and materials science.
讲师
日期
2026年09月18日 至 12月18日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周五 | 14:20 - 17:50 | A14-203 | Zoom 17 | 442 374 5045 | BIMSA |
修课要求
Calculus, linear algebra, probability and statistics, programming fundamentals (Python), basic knowledge of differential equations
课程大纲
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 of 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
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 of 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
参考资料
1. Machine Learning in Modeling and Simulation: Methods and Applications by Timon Rabczuk and Klaus-Jürgen Bathe (Springer, 2023)
2. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
3. Pattern Recognition and Machine Learning by Christopher M. Bishop
4. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
2. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
3. Pattern Recognition and Machine Learning by Christopher M. Bishop
4. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
不公开
语言
英文