Course Overview:
This course delves into the emerging field of Physics-Informed Neural Networks (PINNs), a powerful tool that bridges the gap between artificial intelligence (AI) and scientific computing. You'll explore how PINNs leverage physical laws to enhance the capabilities of neural networks, leading to exciting applications in Supply Chain Management (SCM) for tasks involving physical systems.
Learning Objectives:
Define Physics-Informed Neural Networks (PINNs) and their core principles.
Understand how PINNs integrate physical laws into neural network architectures.
Explore applications of PINNs in SCM for simulating and optimizing physical processes (e.g., transportation, inventory flow).
Gain hands-on experience implementing simple PINNs using popular deep learning libraries (coding exercises).
Analyze the advantages and limitations of PINNs compared to traditional simulation methods.
Course Highlights:
1. Unveiling Physics-Informed Neural Networks
Introduction to PINNs: A marriage of AI and physics for scientific discovery.
Demystifying the PINN Framework: Integrating governing equations (PDEs) into neural network training.
Understanding the loss function in PINNs: Balancing data fidelity and physical law compliance.
Hands-on Coding Exercises: Implementing a basic PINN using a deep learning library (e.g., solving a simple heat transfer equation).
Case Studies: Exploring early applications of PINNs in simulating fluid flow for optimizing transportation networks in SCM.
2. PINNs Applications in SCM and Beyond
Exploring PINNs for optimizing inventory management systems with physical constraints (e.g., storage capacity limitations).
Applications in logistics and transportation: Simulating traffic flow and optimizing delivery routes with PINNs.
Beyond core SCM processes: Exploring PINNs for predictive maintenance of equipment and optimizing resource allocation.
Discussion on the future of PINNs and challenges in integrating physics with deep learning models.
Course Wrap-up: Addressing limitations and responsible AI practices when implementing PINNs in SCM applications.
Prerequisites:
Strong understanding of partial differential equations (PDEs) and numerical methods
Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with fluid mechanics, biomechanics, and physiological modeling concepts