Numerical Recipes and Practical Applications Krishnan, N. M. Anoop. creator author. aut http://id.loc.gov/vocabulary/relators/aut Kodamana, Hariprasad. author. aut http://id.loc.gov/vocabulary/relators/aut Bhattoo, Ravinder. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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electronic resource

access XX, 279 p. 110 illus., 95 illus. in color. online resource. Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials. Part I: Introduction -- Part II: Basics of Machine Learning Methods -- Introduction to Data-Based Modeling -- Model Development -- Introduction to Machine Learning -- Quick Dive into Probabilistic Methods -- Optimization -- Part III: Application in Glass Science -- Property Prediction -- Glass Discovery -- Understanding Glass Physics -- Atomistic Modeling -- Future Directions. by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo. Materials science Data processing Machine learning System theory Mathematical physics Ceramic materials Computational Materials Science Machine Learning Complex Systems Theoretical, Mathematical and Computational Physics Ceramics TA404.23 9783031446221 https://doi.org/10.1007/978-3-031-44622-1 https://doi.org/10.1007/978-3-031-44622-1 240506 20251030112029.0 978-3-031-44622-1