EP30.FSF1108 — Scientific Machine Learning

Scientific Machine Learning (SciML) is an emerging interdisciplinary field that integrates machine learning techniques with scientific computing and physical modeling. The course focuses on combining data-driven methods with physical laws to model, simulate, and analyze complex systems arising in science and engineering.

This course emphasizes the synergy between classical scientific modeling and modern machine learning, with particular attention to physics-informed learning, operator learning, and reduced-order modeling. Students will gain both theoretical understanding and practical skills for applying SciML to real-world scientific problems.

The main topics of the course include data-driven scientific modeling, mathematical foundations for scientific machine learning, physics-informed learning, operator learning, reduced-order modeling, uncertainty quantification, and real-world scientific applications.

Requirements for Final Examination

Students are allowed to take the final/terminal exams (50%) if their attendance score (10%) is 5 or higher (on a 10-point scale).

See Appendix 2, Rubric 1 for attendance evaluation.

Requirements for Class Attendance

Students are responsible for attending all classes. In case of absence due to force majeure, students must provide sufficient and reasonable proof. For each absence, 1 point will be deducted from the attendance score. Students who miss more than 3 classes, with or without valid reasons, will be considered as failing to complete the course and must re-register.

Students will be awarded points for constructive comments. These points will be added to attendance and group assignment scores.

Groups that do not submit their group work will receive a score of 0. Late submissions will incur a deduction for each day they are late.

Requirements for In-class Behaviour

The course is conducted on the principle of mutual respect between learners and lecturers. Any behavior that disrupts the teaching and learning process is strictly prohibited.

Students must arrive on time for class. Those who are more than 10 minutes late will not be allowed to attend. Noise and other disturbances that affect the learning process are not permitted.

Laptops and tablets may only be used for taking notes and performing calculations related to lectures and exercises. Use for any other purpose is strictly prohibited.