Machine Learning for the Physical Sciences 1st Edition

Grabbing the attention of scientists and engineers, Machine Learning for the Physical Sciences, 1st Edition by Carlo Requião da Cunha is a modern roadmap for anyone who wants to harness data-driven methods to solve real-world physical problems. This authoritative title bridges rigorous theory and practical application, making complex machine learning concepts accessible to researchers, graduate students, and professionals in physics, chemistry, materials science, and engineering.

Understandable yet thorough, the book covers essential machine learning techniques tailored to the physical sciences: supervised and unsupervised learning, probabilistic modeling, dimensionality reduction, and approaches to dynamical systems and inverse problems. It emphasizes interpretability, uncertainty quantification, and the interplay between physical laws and data, helping readers build robust models that respect scientific constraints.

Filled with clear explanations, practical examples, and case studies drawn from contemporary research, this edition equips readers to accelerate simulation workflows, discover new materials, and improve experimental design. Whether you’re at a university lab in Europe, a research institute in North America, or an industrial R&D center in Asia, you’ll find strategies that translate across disciplines and geographies.

If you want to elevate your research with machine learning tools that respect the structure of physical systems, this book is an essential resource. Machine Learning for the Physical Sciences, 1st Edition is ideal for scientists seeking a pragmatic, theory-informed guide to data-driven discovery. Order your copy today and start transforming experimental and computational workflows with methods tailored for the physical sciences.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.