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.
Lecturer
Date
16th October, 2026 ~ 8th January, 2027
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Friday | 14:20 - 17:50 | A14-203 | ZOOM 05 | 293 812 9202 | BIMSA |
Prerequisite
Calculus, linear algebra, probability and statistics, programming fundamentals (Python), basic knowledge of differential equations
Syllabus
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
Reference
1. Machine Learning in Modeling and Simulation: Methods and Applications by Timon Rabczuk and Klaus-Jürgen Bathe (Springer, 2023)
2. Introduction to Machine Learning with Python by Andreas C. Müller & Sarah Guido (2017)
3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
4. Pattern Recognition and Machine Learning by Christopher M. Bishop
5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
2. Introduction to Machine Learning with Python by Andreas C. Müller & Sarah Guido (2017)
3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
4. Pattern Recognition and Machine Learning by Christopher M. Bishop
5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Audience
Undergraduate
, Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
No
Notes Public
No
Language
English