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Physics-Informed Machine Learning: A Practical Guide
Complete Edition — Foundations, Advanced Methods, and Applications
This book is 100% completeLast updated on 2026-11-07
+100% in the last 30 days
A practical, code-first guide to physics-informed machine learning for scientists and engineers, now complete in one volume. Build your first PINN from scratch in PyTorch, then go further into hard constraints, Navier–Stokes, neural operators, inverse problems, uncertainty quantification, and real case studies in aerodynamics, materials, and biomedicine.
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Physics-Informed Machine Learning: A Practical Guide is a code-first book for scientists and engineers who know their differential equations but are new to deep learning. You will learn to put the physics you already know directly into a neural network, then build, train, debug, and verify these models yourself, with an honest account of where they fail.
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Kompri Kompri
Kompri is an academic, author, investor, and educator based in Jambi, Indonesia. He holds a doctorate in Education Management and a bachelor’s degree in Physics, and has written and published dozens of books along with numerous other scholarly works. An avid reader, he is one of today’s most productive researchers. Alongside his academic career, he publishes both theoretical and practical books for readers around the world. His work rests on one conviction: knowledge is most valuable when it is clear, usable, and shared, so that anyone, in any country, can put it to work.
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