Zobrazeno 1 - 10
of 1 006
pro vyhledávání: '"65C20"'
Autor:
Kutri, Robert, Scheichl, Robert
Gaussian processes (GPs) and Gaussian random fields (GRFs) are essential for modelling spatially varying stochastic phenomena. Yet, the efficient generation of corresponding realisations on high-resolution grids remains challenging, particularly when
Externí odkaz:
http://arxiv.org/abs/2412.07929
We introduce a counting process to model the random occurrence in time of car traffic accidents, taking into account some aspects of the self-excitation typical of this phenomenon. By combining methods from probability and differential equations, we
Externí odkaz:
http://arxiv.org/abs/2410.00446
Autor:
Bistrian, Diana A.
Publikováno v:
Transylvanian Journal of Mathematics and Mechanics, 14 (2), 105-115, 2022
This paper introduces the approach of Randomized Orthogonal Decomposition (ROD) for producing twin data models in order to overcome the drawbacks of existing reduced order modelling techniques. When compared to Fourier empirical decomposition, ROD pr
Externí odkaz:
http://arxiv.org/abs/2410.02813
We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an invers
Externí odkaz:
http://arxiv.org/abs/2409.09137
Publikováno v:
Transylvanian Journal of Mathematics and Mechanics, Vol.15, No. 1-2, pp. 17-27, 2023
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output using Koopman operator theory. A reduced-order modelling method that in
Externí odkaz:
http://arxiv.org/abs/2409.03549
This work considers the computation of risk measures for quantities of interest governed by PDEs with Gaussian random field parameters using Taylor approximations. While efficient, Taylor approximations are local to the point of expansion, and hence
Externí odkaz:
http://arxiv.org/abs/2408.06615
Autor:
Nagaraj, Sriram, Hickok, Truman
This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Ae
Externí odkaz:
http://arxiv.org/abs/2406.15619
Autor:
Auestad, Øyvind Stormark
We propose a new type of fully discrete finite element approximation of a class of semilinear stochastic parabolic equations with additive noise. Our discretization differs from the ones typically considered, in that we employ a nested finite element
Externí odkaz:
http://arxiv.org/abs/2406.11041
Autor:
Gazzani, Guido, Guyon, Julien
We consider the path-dependent volatility (PDV) model of Guyon and Lekeufack (2023), where the instantaneous volatility is a linear combination of a weighted sum of past returns and the square root of a weighted sum of past squared returns. We discus
Externí odkaz:
http://arxiv.org/abs/2406.02319
Autor:
Nguyen, Du, Sommer, Stefan
We specify the conditions when a manifold M embedded in an inner product space E is an invariant manifold of a stochastic differential equation (SDE) on E, linking it with the notion of second-order differential operators on M. When M is given a Riem
Externí odkaz:
http://arxiv.org/abs/2406.02879