Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Graziani, Carlo"'
Autor:
Ngom, Marieme, Graziani, Carlo
We introduce OS-net (Orbitally Stable neural NETworks), a new family of neural network architectures specifically designed for periodic dynamical data. OS-net is a special case of Neural Ordinary Differential Equations (NODEs) and takes full advantag
Externí odkaz:
http://arxiv.org/abs/2309.14822
Autor:
Graziani, Carlo, Ngom, Marieme
Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from t
Externí odkaz:
http://arxiv.org/abs/2205.14208
We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information
Externí odkaz:
http://arxiv.org/abs/2203.12529
Tomographic reconstruction, despite its revolutionary impact on a wide range of applications, suffers from its ill-posed nature in that there is no unique solution because of limited and noisy measurements. Therefore, in the absence of ground truth,
Externí odkaz:
http://arxiv.org/abs/2103.15864
Publikováno v:
Commun. Appl. Math. Comput. Sci. 17 (2022) 1-41
We present a new polynomial-free prolongation scheme for Adaptive Mesh Refinement (AMR) simulations of compressible and incompressible computational fluid dynamics. The new method is constructed using a multi-dimensional kernel-based Gaussian Process
Externí odkaz:
http://arxiv.org/abs/2003.08508
We present a scheme by which a probabilistic forecasting system whose predictions have poor probabilistic calibration may be recalibrated by incorporating past performance information to produce a new forecasting system that is demonstrably superior
Externí odkaz:
http://arxiv.org/abs/1904.02855
We present a new finite difference shock-capturing scheme for hyperbolic equations on static uniform grids. The method provides selectable high-order accuracy by employing a kernel-based Gaussian Process (GP) data prediction method which is an extens
Externí odkaz:
http://arxiv.org/abs/1808.04960
Autor:
Bott, Archie F. A., Tzeferacos, Petros, Chen, Laura, Palmer, Charlotte A. J., Rigby, Alexandra, Bell, Anthony R., Bingham, Robert, Birkel, Andrew, Graziani, Carlo, Froula, Dustin H., Katz, Joseph, Koenig, Michel, Kunz, Matthew W., Li, Chikang, Meinecke, Jena, Miniati, Francesco, Petrasso, Richard, Park, Hye-Sook, Remington, Bruce A., Reville, Brian, Ross, J. Steven, Ryu, Dongsu, Ryutov, Dmitri, Séguin, Fredrick H., White, Thomas G., Schekochihin, Alexander A., Lamb, Donald Q., Gregoria, Gianluca
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Mar . 118(11), 1-12.
Externí odkaz:
https://www.jstor.org/stable/27027585
We introduce an entirely new class of high-order methods for computational fluid dynamics (CFD) based on the Gaussian Process (GP) family of stochastic functions. Our approach is to use kernel-based GP prediction methods to interpolate/reconstruct hi
Externí odkaz:
http://arxiv.org/abs/1611.08084
Proton radiography is an important diagnostic method for laser plasma experiments, and is particularly important in the analysis of magnetized plasmas. The theory of radiographic image analysis has heretofore only permitted somewhat limited analysis
Externí odkaz:
http://arxiv.org/abs/1603.08617