Physics-Informed Neural Networks M. Raissi & P. Perdikaris & G.E. Karniadakis

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• Author(s): M. Raissi, P. Perdikaris, G.E. Karniadakis

“Physics-Informed Neural Networks and Biologically-Inspired Machine Learning” is a comprehensive guide that bridges the gap between physics-based knowledge and cutting-edge machine learning techniques. This book explores the integration of biological principles into AI systems, harnessing the power of neural networks for solving complex physics and engineering problems. Through in-depth case studies, readers will gain insights into real-world applications, from fluid dynamics simulations to quantum mechanics. Emphasizing ethical considerations and responsible AI, this book is an essential resource for researchers, engineers, and students exploring the exciting intersection of physics and machine learning.

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