MTH 602: Scientific Machine Learning - fall
Prerequisites: EAS 520/DSC 520, EAS 501, and EAS 502, or permission of the instructor
Scientific machine learning algorithms for computational science and engineering. Topics may include physics-informed neural networks, neural dynamical systems, AI-based surrogate models, signal detection with convolutional neural networks, learning nonlinear continuous operators, neural turbulence models, optimization algorithms, simulation-based Bayesian inference, and more. Python will be the primary language. Emphasis on real-world applications, covering high-performance computing with multi-core and GPU acceleration.
| Class# | Sct | Type | Seats | Units | ||||
|---|---|---|---|---|---|---|---|---|
| 13757 | 01 | Lecture | 20 | 3.00 | ||||
| Days | Start | End | Location | |||||
| MON TUE WED THU FRI SAT | 11:00 AM EDT | 12:30 PM EDT | TEX-001 | |||||
| Instructor: Scott Field | Class status: O | |||||||
| Prerequisites: EAS 520/DSC 520, EAS 501, and EAS 502, or permission of the instructor | ||||||||
| Enrollment Section | ||||||||
| Class instruction mode: In Person | ||||||||