Computational Modeling and Digital Twins with AI (Paperback) (ISBN-13: 9798295450389)

Computational Modeling and Digital Twins with AI

Key Points: Computational Modeling and Digital Twins with AI

• Definition and Evolution of Digital Twins
• Core Characteristics of Digital Twins.
• Value Proposition and Industry Impact
  • Enhanced monitoring, predictive maintenance, and performance optimization.
  • Accelerated design cycles and improved decision-making.
  • Tangible cost savings, increased efficiency, and sustainability benefits across sectors like aerospace, automotive, manufacturing, energy, healthcare, construction, logistics, and agriculture.
• Model Fidelity and Abstraction
  • Fidelity refers to how accurately the digital twin mirrors its physical counterpart, across geometric, behavioral, state, contextual, and data dimensions.
  • The level of abstraction and granularity is purpose-driven, balancing detail with computational feasibility.
• Physics-Based and Data-Driven Modeling
  • Physics-based models use fundamental laws (e.g., conservation, constitutive relations) for deterministic, interpretable predictions.
  • Data-driven models leverage empirical data and machine learning to capture complex, real-world behaviors.
  • Hybrid modeling combines both approaches for greater accuracy and adaptability.
• Physics-Informed Machine Learning (PIML)
  • PIML integrates physical laws into machine learning models, improving generalization, reducing data requirements, and ensuring physically plausible predictions.

Used for complex simulations in fluid dynamics, structural mechanics, and materials science.