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PINN with Python: An Introduction - Physics-Informed Neural Networks

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Description

Physics-Informed Neural Networks (PINNs) represent a breakthrough approach to solving complex scientific and engineering problems by combining neural networks with the fundamental rigour of physical laws. This comprehensive guide introduces you to PINNs, a technique that embeds partial differential equations (PDEs), boundary conditions, and initial conditions directly into a neural network's training process through custom loss functions. The approach enables neural networks to learn and obey the laws of physics whilst solving real-world problems. Written by Scientech Publisher LLC, this book provides essential knowledge for machine learning practitioners, researchers, and engineers seeking to integrate physics-based constraints into their neural network models. Whether you're tackling fluid dynamics, structural mechanics, heat transfer, or other PDE-governed systems, this introduction equips you with the foundational concepts and practical methods to implement physics-informed solutions using Python.

Key Features

This PINN with Python book teaches how to embed partial differential equations directly into neural network training via custom loss functions.

Learn how Physics-Informed Neural Networks enforce physical laws and boundary conditions during the learning process for scientifically accurate solutions.

The guide combines neural network flexibility with rigorous physical constraints, enabling you to solve complex engineering and scientific problems.

Scientech Publisher LLC provides practical introduction to PINNs suitable for machine learning practitioners and researchers building physics-aware AI models.

Understand solution function representation through neural networks that satisfy both data observations and fundamental equations of physics.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
19 August 2025
Listed Since
23 August 2025

Barcode

No barcode data available

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