"Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material including lecture videos per section, homeworks, data, and codes in MATLAB, Python, and Julia available on databookuw.com"--

Before Bill Gates became an expert on climate science, he was known as one of the few who studied pandemics - how they start, how they spread, how they can be controlled. He warned us years ago in a now-famous TED Talk of their arrival in our future. The future, of course, is now, and now is when we have to plan against a next one. HOW TO PREVENT THE NEXT PANDEMIC is a clear and upbeat plan of what every country, every government leader, and every individual can do in order to help prevent another pandemic, grounded in Bill's firsthand experience with the Gates Foundation's commitment to fighting Covid-19.

DescriptionContentsResourcesCoursesAbout the Authors Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. Accompanying the mathematical derivations are full implementations of dozens of numerical methods in Python, as well as more than 250 end-of-chapter problems. Numerical methods and physics examples are clearly separated, allowing this introductory book to be later used as a reference; the penultimate section in each chapter is an in depth project, tackling physics problems which cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

The Matter of Everything, accelerator physicist Suzie Sheehy introduces us to the people who, through a combination of genius, persistence and luck, staged the ground-breaking experiments of the twentieth century that changed the course of history. From the serendipitous discovery of X-rays in a German laboratory, to the scientists trying to prove Einstein wrong (and inadvertently proving him right), to the race to split open the atom, Sheehy shows how our most brilliant, practical physicists have shaped innumerable aspects of how we live today. Radio, TV, the chips in our smartphones, MRI scanners, radar equipment and microwaves, to name a few: these were all made possible by their determination to understand, and control, the microscopic --