Scientific Machine Learning Foundations, Algorithms, and Applications
Fr. 213.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
23.07.2026
Verlag
Springer Nature SingaporeSeitenzahl
813 (Printausgabe)
Dateigröße
20087 KB
Sprache
Englisch
EAN
9789819578566
This book highlights and addresses a crucial need in the emerging field of Scientific Machine Learning (SciML) by offering a comprehensive and accessible guide that blends theory, algorithms, and applications. It explores how the synergy between machine learning and scientific computing can lead to more accurate, interpretable, and efficient models for scientific discovery. The book covers foundational mathematical principles, physics-informed neural networks, optimization techniques, uncertainty quantification, deep learning for scientific data, transformer-based foundational models, and neuro-symbolic reasoning. By combining domain knowledge with modern AI, SciML opens new frontiers in disciplines such as physics, biology, and engineering. This book is an essential resource for students, researchers, and professionals aiming to apply AI in scientific domains.
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.