PINN with Python: An Introduction
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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 issues, and developing robust training strategies.
Benefit directly from the author's research experience, insights, and practical utility codes—all integrated into this invaluable resource.
Whether you're a researcher pushing boundaries, a student eager to grasp cutting-edge computational methods, or a practitioner seeking advanced solutions, this book will equip you with the essential tools and understanding to deploy PINNs effectively across a wide range of PDE-driven challenges.
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Dr. G.R. (Gui-Rong) Liu is an expert in computational mechanics, particularly known for pioneering work in meshfree methods, smoothed finite element methods (S-FEM), and particle-based simulations. He currently serves as a Professor of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati. A prolific researcher and educator, Dr. Liu is recognized for pioneering meshfree methods, smoothed finite element methods (S-FEM), and smoothed particle hydrodynamics (SPH). These innovations have significantly advanced simulation techniques in both solid and fluid mechanics.
In recent years, Dr. Liu has authored several textbooks and reference works for courses and research in areas such as artificial intelligence, machine learning, mathematics, computational methods, mechanics of materials, solid mechanics, engineering mechanics, applied mechanics, and fluid dynamics.
