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Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research.A basic example of this is quantum state tomography, where a quantum state is learned from measurement. [1]
"High school physics textbooks" (PDF). Reports on high school physics. American Institute of Physics; Zitzewitz, Paul W. (2005). Physics: principles and problems. New York: Glencoe/McGraw-Hill. ISBN 978-0078458132
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
It is the first book in a series called The Theoretical Minimum, based on Stanford Continuing Studies courses taught by world renowned physicist Leonard Susskind. The courses collectively teach everything required to gain a basic understanding of each area of modern physics, including much of the fundamental mathematics.
In 2011, MIT OpenCourseWare introduced the first of fifteen OCW Scholar courses, which are designed specifically for the needs of independent learners. While still publications of course materials like the rest of the site content, these courses are more in-depth and the materials are presented in logical sequences that facilitate self-study.
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