Author Description

Physics-Informed Neural Networks (PINNs) are transforming the way we solve complex scientific and engineering problems. This book serves as your essential guide to understanding this powerful technique, which elegantly combines the flexibility of neural networks with the fundamental rigor of physical laws. PINNs embed partial differential equations (PDEs), along with their boundary and initial conditions, directly into a neural network's training process via a custom loss function. This means the neural network learns to obey the laws of physics! The solution function is represented by a neural network-a smooth, differentiable model constructed from affine transformations and activation functions. This formulation allows the network to intrinsically satisfy PDEs. Crucially, the derivatives required to enforce these physical constraints are computed using autograd, a lightning-fast, machine-precision technique built into modern machine learning libraries. This comprehensive and practical book delivers: * Core Theory & Formulations: Understand the foundational principles that make PINNs work. * Essential Techniques: Learn methods for building, training, and applying PINN models. * Step-by-Step Coding: Get hands-on with Python code to implement PINNs from scratch. * Collocation vs. Energy-Based PINNs: Discover the nuances between these two primary approaches, their strengths, and when to use each. The book dives deep into applications across key PDE types critical in science and engineering: * Static Problems: Tackle Poisson equations. * Time-Dependent Systems: Solve heat equations (parabolic) and wave equations (hyperbolic). * Eigenvalue Challenges: Solve Helmholtz equations. Beyond theoretical concepts, you'll explore in-depth case studies demonstrating how to construct effective PINN models for various PDE types and geometries. We also address real-world challenges, including designing appropriate loss functions, normalizing equation systems, resolving convergence i