Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.
This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.
Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.
This book bridges the gap between traditional engineering disciplines and modern machine learning (ML) techniques, offering a comprehensive introduction to how AI can solve complex engineering problems. With a focus on physics-informed machine learning and explainable AI (XAI), it aims to equip engineers with the skills to integrate data-driven approaches into their workflows while respecting the principles of engineering systems.
Key Themes of the Book
1. The Role of Machine Learning in Engineering
Why Engineers Need Machine Learning:
Traditional computational methods often struggle with high-dimensional problems, noisy data, and real-time predictions.
ML provides powerful tools to model complex systems, optimize processes, and predict outcomes with greater accuracy.
Challenges in Engineering Applications:
Integration of domain knowledge (e.g., physics laws) into ML.
The need for models that are not only accurate but also interpretable and trustworthy.
2. Introduction to Physics-Informed Machine Learning
Physics-informed machine learning (PIML) integrates known physical laws (e.g., conservation laws, boundary conditions) into the learning process, ensuring that ML models respect underlying physical principles.
What is PIML?
Combines data-driven methods with first-principle physics models.
Useful for problems with limited data but strong domain constraints.
Applications of PIML:
Computational fluid dynamics (CFD).
Structural health monitoring.
Material design and optimization.
Techniques in PIML:
Physics-Informed Neural Networks (PINNs): Incorporates partial differential equations (PDEs) as loss functions.
Hybrid Models: Combines machine learning with physics-based simulations.
3. Explainable AI (XAI) for Engineers
Why Explainability Matters:
Engineers need to trust and understand ML models, especially for safety-critical systems (e.g., aviation, power grids).
Regulatory and ethical considerations demand transparency.
Explainability Techniques:
Post-hoc methods: Tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations).
Intrinsic interpretability: Using simpler models like decision trees or physics-guided architectures.
Case Studies:
Explaining material failure predictions.
Interpreting predictive maintenance models for mechanical systems.
4. Machine Learning Techniques for Engineering Applications
The book explores ML algorithms tailored to engineering use cases:
Supervised Learning:
Regression and classification for failure prediction and fault detection.
Unsupervised Learning:
Clustering and anomaly detection in sensor data.
Deep Learning:
Neural networks for modeling complex relationships in structural analysis and fluid mechanics.
Reinforcement Learning:
Optimizing control systems for robotics and autonomous vehicles.
5. Practical Implementation Using Python
The book emphasizes hands-on learning through Python-based examples and tutorials:
Popular Libraries:
TensorFlow and PyTorch for model development.
Scikit-learn for classical ML techniques.
Specialized libraries like SimPy for simulation modeling and OpenFOAM for CFD integration.
Building Physics-Informed Models:
Examples of integrating physics constraints into neural network training.
Model Deployment:
Techniques for deploying ML models in real-time engineering systems.
Hard Copy: Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications