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 from Translational AI Center at Iowa State University.

For more information regarding the course including: Learning Outcomes, Assessments, and a Course Outline please visit the Scientific Machine Learning course page from Iowa State Online.

Prerequisites

• Basic Python programming
• Basic understanding of numerical methods and deep learning

Intended Audience
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.