It contains 15 lectures totaling about 30-35 hours, homework, and 20 projects across different fields. Materials in the kit include the fundamentals of deep learning as well as advanced topics and hands-on exercises. Overview of the Science and Engineering Teaching KitĮducators in various fields will find this teaching kit useful, from mechanical, structural, and electrical engineering to atmospheric science, computational science, and more. The entire lecture portion of the course is also available on NVIDIA On-Demand. Kits include lecture materials, labs, and sample problem sets to facilitate incorporating advanced technology into course curriculums. “We focused on regression and mimicking the approximation theory and algorithms required in classical numerical analysis courses in the engineering curriculum.”Įducators can get full, free access to the Deep Learning for Science and Engineering Teaching Kit (and many more) by joining the NVIDIA DLI Teaching Kit Program. Raj Shukla, to address the urgent need for specific material for scientists and engineers,” said Karniadakis. “We designed this course with my collaborator, Dr. It was created with leading academics including George Karniadakis, professor of Applied Mathematics and Engineering at Brown University, and his team. This new teaching kit will enable the next generation of engineers and scientists to leverage AI for innovation in the field of engineering and science.

To support this work, NVIDIA is collaborating with pioneers at the intersection of science, engineering, and AI to create the first Deep Learning for Science and Engineering Teaching Kit for educators in academia worldwide. There is no denying that today’s students who want to succeed in tomorrow’s workplace need to understand how AI can enable solutions. Physics-ML is used to model physical systems such as weather, ocean currents, and tidal activityĪcademic institutions play a pivotal role in nurturing emerging technologies and driving the innovation needed for their widespread adoption. It is well suited for modeling real-world systems - some applications include: predicting extreme weather, data center cooling, turbulent flow over a car, and protein modeling.

Physics-informed machine learning (physics-ML) leverages knowledge of the physical world to train AI models. It also has broad applications in engineering and science.

AI powers chatbots, recommender systems, computer vision applications, fraud prevention, and autonomous vehicles. AI is quickly becoming an integral part of diverse industries, from transportation and healthcare to manufacturing and finance.

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