A Hands-on Approach to Scientific Artificial Intelligen Programming

This book brings together the teaching materials used in the courses on scientific artificial intelligence delivered by the Keysight-UZ Chair of National AI Strategy of the Ministry of Digital Transformation at the University of Zaragoza during the years of its existence, from 2024 onwards.

As will be seen, the material takes a predominantly practical approach and is designed to enable students to experiment with all the modules in the programme. This course is designed for postgraduate students. Many aspects will be better understood if students have been introduced to basic concepts of physics during their undergraduate studies, particularly mechanics, which is the focus of this book. If they have a background in scientific computing—and particularly in computational engineering—and are familiar with the finite element, finite volume, and finite difference methods, they are sure to find the content both accessible and engaging!

The field of scientific artificial intelligence is fascinating. The possibility that artificial intelligence can make predictions—whether using experimental or synthetic data, or relying solely on the differential equations that govern the phe-nomena under study—is, quite simply, fascinating. The challenge is for this artificial intelligence to be truly predictive in an engineering sense: that the pre-dictions it makes can be verified and validated, so that industry can ultimately place its trust in the benefits this new discipline can bring.

It is the task of engineers, physicists, and mathematicians to turn this family of techniques into the tool that industry needs right now. The responsibility lies with us, and it begins with training a new generation of researchers in the field who will, no doubt, bring this to a successful conclusion.