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portada Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics
Type
Physical Book
Publisher
Author
Year
2026
Language
English
Pages
169
Format
Paperback
ISBN13
9781611979169

Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics

Philippe G. Lefloch;Jean-Marc Mercier;Shohruh Miryusupov (Author) · SIAM - Society for Industrial and Applied Mathematics · Paperback

Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics - Philippe G. LeFloch;Jean-Marc Mercier;Shohruh Miryusupov

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Synopsis "Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics"

This monograph develops a unified, application-driven framework for kernel methods grounded in reproducing kernel Hilbert spaces and optimal transport. The primary goal is to tackle industrial cases from computational physics and mathematical finance and discuss applications across various areas, such as statistics, or artificial intelligence (physics-informed systems, reinforcement learning, machine learning, generative methods, etc.).

Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics is divided into two parts, theoretical principles and the techniques employed in their applications; contains numerous applications in engineering, finance, and machine learning; and provides a framework for designing numerically efficient, large-scale dataset strategies.

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