September 9
@
8:00 am
–
5:00 pm
Dive into the theory, implementation, and limitations of Scientific Machine Learning (SciML) models. In this course, you will engage in hands-on and practical activities under the expert guidance of our experienced instructors. Learning SciML will be enjoyable and rewarding as you engage in real-world applications, such as solving partial differential equations using physics-informed neural networks (PINNs) and neural operators (PyTorch implementation). By the end of this course, you’ll not only understand the theory behind SciML but also have the confidence and expertise to tackle any partial differential equation with PyTorch.
This course is part of a micro-credential program pilot.
- Basic Python programming
- Basic understanding of numerical methods and deep learning
The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a basic understanding of AI and how to use PyTorch for a broad range of audiences.
Expertise level: Advanced
Any of the courses in the series is available for credit (each 1 credit and maximum 3 credits per semester) as an independent study (ME 590Z) under Dr. Aditya Balu. Interested students can register for the independent study by emailing benearl@iastate.edu and cc-ing baditya@iastate.edu. Also, please fill out this form for our records: https://forms.gle/FBVZkZJqSmU6ofod7
Industry Professionals/ISU Staff/Postdocs, etc.: non-credit through ISU Online for an introductory promo price of $500
$500
