Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics
$229.83
- Paperback
169 pages
- Release Date
17 July 2026
Summary
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 Learn…
Book Details
| ISBN-13: | 9781611979169 |
|---|---|
| ISBN-10: | 1611979161 |
| Author: | Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov |
| Publisher: | Society for Industrial & Applied Mathematics,U.S. |
| Imprint: | Society for Industrial & Applied Mathematics,U.S. |
| Format: | Paperback |
| Number of Pages: | 169 |
| Release Date: | 17 July 2026 |
Philippe G. LeFloch
P.G. LeFloch
P.G. LeFloch is a research professor at the Laboratoire Jacques-Louis Lions, Sorbonne University, and at the Centre National de la Recherche Scientifique (CNRS).
J.-M. Mercier and S. Miryusupov
J.-M. Mercier and S. Miryusupov are permanent researchers at the financial company MPG Partners, based in Paris.
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