Results 51 to 60 of about 30,004 (201)

Polynomial Chaos Expansion of random coefficients and the solution of stochastic partial differential equations in the Tensor Train format

, 2015
We apply the Tensor Train (TT) decomposition to construct the tensor product Polynomial Chaos Expansion (PCE) of a random field, to solve the stochastic elliptic diffusion PDE with the stochastic Galerkin discretization, and to compute some quantities of
Dolgov, Sergey, Khoromskij, Boris N., Litvinenko, Alexander, Matthies, Hermann G.   +3 more
core   +1 more source

A conditional stochastic projection method applied to a parametric vibrations problem

Journal of Civil Engineering and Management, 2014
Parametric vibrations can be observed in cable-stayed bridges due to periodic excitations caused by a deck or a pylon. The vibrations are described by an ordinary differential equation with periodic coefficients.
Wlodzimierz Brzakala, Aneta Herbut
doaj   +1 more source

Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling

Algorithms, 2020
Geomechanical modelling of the processes associated to the exploitation of subsurface resources, such as land subsidence or triggered/induced seismicity, is a common practice of major interest.
Claudia Zoccarato, Laura Gazzola, Massimiliano Ferronato, Pietro Teatini   +3 more
doaj   +1 more source

Magnetometric resistivity tomography using chaos polynomial expansion [PDF]

Geophysical Journal International, 2020
SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low ...
Vu, M, Jardani, Abderrahim, Revil, A, Jessop, M   +3 more
openaire   +3 more sources

Robust Design of Suspension System with Polynomial Chaos Expansion and Machine Learning

Наука и техника, 2020
During the early development of a new vehicle project, the uncertainty of parameters should be taken into consideration because the design may be perturbed due to real components’ complexity and manufacturing tolerances. Thus, the numerical validation of
H. Gao, L. Jézéque, E. Cabrol, B. Vitry   +3 more
doaj   +1 more source

On polynomial chaos expansion via gradient-enhanced ℓ1-minimization [PDF]

Journal of Computational Physics, 2016
Gradient-enhanced Uncertainty Quantification (UQ) has received recent attention, in which the derivatives of a Quantity of Interest (QoI) with respect to the uncertain parameters are utilized to improve the surrogate approximation. Polynomial chaos expansions (PCEs) are often employed in UQ, and when the QoI can be represented by a sparse PCE, $\ell_1$-
Peng, Ji, Hampton, Jerrad, Doostan, Alireza   +2 more
openaire   +3 more sources

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System

Advanced Intelligent Systems, EarlyView.
This work introduces a memristor‐based reservoir computing (RC) system for real‐time, energy‐efficient processing of neural signals in brain‐machine interface (BMI). Combined with flexible mesh neural probes with tissue‐like flexibility and subcellular‐scale features that enable consistent, long‐term tracking of single‐cell neural activities, the ...
Soohyeon Kim, Yuting Wu, Finn Moore, Mingtao Hu, Xiaoyu Zhang, Sangmin Yoo, Eric Yeu‐Jer Lee, Hao Shen, Yichun He, Jia Liu, Wei D. Lu   +10 more
wiley   +1 more source

Logarithmic Gradient Transformation and Chaos Expansion of Ito Processes [PDF]

, 2018
Since the seminal work of Wiener, the chaos expansion has evolved to a powerful methodology for studying a broad range of stochastic differential equations. Yet its complexity for systems subject to the white noise remains significant.
Gorji, M. H.
core   +2 more sources

Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley   +1 more source