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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Scientific Machine Learning
• Podnázev: Foundations, Algorithms, and Applications
• Autor: Bikram Pratim Bhuyan, Amylia Ait Saadi, Yassine Meraihi, Amar Ramdane-Cherif
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 740
• EAN: 9789819578559
• ID: 50799268
• Nakladatelství: Springer, Berlin
• Rozměry: 235 × 155 mm
• Datum vydání: 08. August 2026

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