From the 1 of 11 linked papers with an AI index.
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Physics-informed token transformer methodology for nonlinear balance laws. I. Schwarzschild--Burgers fluid flows
Philippe G. LeFloch, Shuyang Xiang
We introduce a Physics-Informed Token Transformer (PITT) methodology for nonlinear hyperbolic balance laws in one space dimension, using piecewise steady-state profiles for the rep…
Reproducing kernel methods for machine learning, PDEs, and statistics
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
This monograph develops a unified, application-driven framework for kernel methods grounded in reproducing kernel Hilbert spaces (RKHS) and optimal transport (OT). Part I lays the…
A class of kernel-based scalable algorithms for data science
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which, most importantly, are scale up efficiently with la…
Extrapolation and generative algorithms for three applications in finance
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
For three applications of central interest in finance, we demonstrate the relevance of numerical algorithms based on reproducing kernel Hilbert space (RKHS) techniques. Three use c…