Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Computational Differential Equations with AI: Modern Machine Learning Approaches

Computational Differential Equations with AI: Modern Machine Learning Approaches

Chakraverty, Snehashish
Kumar Samota, Sandeep
Gupta, Reema

Hardcover

Series: Synthesis Lectures on Mathematics & Statistics

General MathematicsProbability & Statistics

PREORDER - Expected ship date October 28, 2026

ISBN10: 3032225736
ISBN13: 9783032225733
Publisher: Springer
Published: Oct 28 2026
Pages: 132
Language: English

This book provides cutting-edge machine learning (ML) methods, including Physics-Informed Neural Networks (PINNs), Neural Operators, and other ML methods, for solving ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic systems. Differential equations (DEs) are the basic foundation for modeling real-world problems in various fields such as physics, engineering, finance, and biology. Solving DEs requires complicated mathematical methods; however, ML is now a viable, innovative, and alternative technique. This book aims to bridge the gap between DEs and ML by explaining how to utilize neural networks, physics-informed models, and other artificial intelligence (AI) based techniques to solve DEs more efficiently and accurately. With the use of ML techniques, readers can also uncover hidden patterns within the data of the problem.The authors utilize Python throughout to implement and demonstrate the methods behind the various presented examples. This book is an ideal choice for academic researchers, engineers, data scientists, and others who are interested in ML methods and real-world applications to democratize next-generational computational mathematics.

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