Course Overview:
This course introduces you to Physics-Informed Neural Networks (PINNs), a cutting-edge technique that merges the power of neural networks with the fundamental laws of physics. You'll explore how PINNs can be used to analyze complex marketing-related phenomena, optimize pricing strategies based on physical constraints, and unlock new possibilities for data-driven decision making in Sales Management.
Learning Objectives:
Define Physics-Informed Neural Networks (PINNs) and their unique ability to learn from both data and physical laws.
Understand the core concepts behind PINNs, including incorporating governing equations into neural network training.
Explore applications of PINNs in Marketing, Pricing Strategy, and Sales Management (e.g., simulating customer behavior based on physical models, optimizing pricing for resource allocation).
Analyze the advantages and limitations of PINNs compared to traditional machine learning techniques for marketing and sales tasks.
Identify the potential of PINNs for future advancements in AI-powered simulations and data analysis within marketing and sales.
Course Highlights:
1. Bridging Physics and AI with PINNs
Introduction to Physics-Informed Neural Networks: Understanding the core concepts and their ability to leverage physical laws for data analysis in marketing contexts.
Demystifying the PINN Architecture: Exploring how PINNs integrate governing equations (e.g., diffusion, optimization models) into neural network training.
Hands-on Exercises (Optional): Utilizing online tools or simplified code examples to explore basic PINN functionalities (e.g., simulating customer behavior based on a physical model).
Case Studies: Examining how companies leverage PINNs for tasks like optimizing product distribution networks based on physical constraints or analyzing customer flow patterns within physical stores.
2. PINNs for Marketing, Pricing & Sales Applications
Exploring Applications in Marketing: Utilizing PINNs for tasks like simulating customer behavior in response to marketing campaigns based on physical models (e.g., diffusion of brand awareness), or optimizing marketing resource allocation based on budget constraints.
Unveiling the Potential for Sales & Pricing: Exploring applications of PINNs in sales forecasting by incorporating physical limitations (e.g., sales team capacity) and developing dynamic pricing models that consider resource availability.
The Future of PINNs in Marketing & Sales: Discussing emerging trends in AI-powered simulations and their potential impact on optimizing marketing strategies and sales performance.
Course Wrap-up: Addressing limitations of PINNs, potential challenges in incorporating physical laws, and best practices for responsible AI implementation in marketing, pricing, and sales.
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