• Tuotekuvaus

  Bridging Physics and AI: Data Science, Machine Learning, and Computational ModellingA unifying framework for understanding how physics and artificial intelligence converge to drive the future of scientific discovery. This book presents a coherent and integrated approach to physics and artificial intelligence, demonstrating how fundamental principles—dynamics, energy, uncertainty, and symmetry—extend naturally into modern machine learning and data science. Grounding AI methods in physical intuition enables readers to move seamlessly from theory to application. Spanning foundational concepts to advanced topics such as generative models, causal inference, and high-performance computing, the text equips readers with both the conceptual insight and practical tools needed to model complex systems, design efficient experiments, and extract meaning from high-dimensional data. The second edition has been significantly expanded to reflect the field's rapid evolution. It introduces scientific machine learning, physics-informed neural networks (PINNs), generative models (VAEs, GANs, diffusion models), causal inference, and AI-driven discovery pipelines, while also addressing the growing importance of big data, computational infrastructure, and ethical considerations in scientific practice. Designed for students, researchers, and professionals, Bridging Physics and AI offers a rigorous yet accessible pathway into one of the most transformative interdisciplinary domains of our time—empowering readers not only to apply these tools, but to understand and shape their future development. Key Features Physics-first approach to artificial intelligence and machine learningClear connections between energy landscapes and optimization, and dynamical systems and learning algorithmsComprehensive coverage of modern AI techniques—including generative models and AI-driven discovery—within a rigorous scientific frameworkStrong emphasis on practical application, intuition, and conceptual clarityDesigned for both self-learning and classroom instructionIntegrated treatment of ethics, interpretability, and trust in scientific AICoverage of high-performance computing and big data in scientific contextsIncludes comprehensive appendices on computational tools and pedagogy

• Tuotetiedot

  • Kustantaja / Valmistaja Taylor & Francis Ltd
  • ISBN 9781041119692
  • Tuotekoodi 9781041119692
  • Alanimike Data Science, Machine Learning, and Computational Modelling
  • Kirjoittajat Ijaz A. Rauf
  • Kieli Englanti
  • Thema-luokitus Fysiikka; Säätötekniikka; Tiedon louhinta; Tekoäly
  • Ilmestymispäivä 02.02.2027
  • Vuosi 2027
  • Painos 2
  • Tuoteryhmä Ulk tiede, luonto
  • Tuotepääryhmä Ulkomaiset tietokirjat
  • Tuotelinja Kirjat
  • Sivumäärä 328
  • Pituus (mm) 234
  • Leveys (mm) 156
  • Paino (g) 453
  • Tuotemuoto Pehmeäkantinen
  • Tuoteturvallisuusyhteystiedot Taylor & Francis Verlag GmbH, GPSR@taylorandfrancis.com, Kaufingerstraße 24, 80331 München, Germany, 80331, München
  • ALV 13,5%

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