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College of Engineering
Mechanical Engineering
SCIML 1
SciML - Scientific Machine Learning
Mark
This educational resource is no longer available.
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.
Pages / Length:
4 modules
Publication Date:
08/2024
Available Stock:
Permanent link for this product:
https://store.extension.iastate.edu/product/17063
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