Results 151 to 160 of about 720 (191)

A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions. [PDF]

BMC Res Notes
Deresse AT, Bekela AS.
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Computational analysis of a class of singular nonlinear fractional multi-order heat conduction model of the human head. [PDF]

Sci Rep
Izadi M, Atangana A.
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Investigating the use of physics informed neural networks for dam-break scenarios. [PDF]

PLoS One
Mumtaz K, Nadeem MW, Khan A, Lakdawala Z.   +3 more
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A primer on variational inference for physics-informed deep generative modelling. [PDF]

Philos Trans A Math Phys Eng Sci
Glyn-Davies A, Vadeboncoeur A, Akyildiz OD, Kazlauskaite I, Girolami M.   +4 more
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Computational Aspects of Stochastic Collocation with Multifidelity Models

SIAM/ASA Journal on Uncertainty Quantification, 2014
In this paper we discuss a numerical approach for the stochastic collocation method with multifidelity simulation models. The method we consider was recently proposed in [A. Narayan, C. Gittelson, and D. Xiu, SIAM J. Sci. Comput., 36 (2014), pp. A495--A521] to combine the computational efficiency of low-fidelity models with the high accuracy of high ...
Xueyu Zhu, Akil Narayan 0001, Dongbin Xiu   +2 more
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A Stochastic Collocation Algorithm with Multifidelity Models

SIAM Journal on Scientific Computing, 2014
We present a numerical method for utilizing stochastic models with differing fideli- ties to approximate parameterized functions. A representative case is where a high-fidelity and a low-fidelity model are available. The low-fidelity model represents a coarse and rather crude ap- proximation to the underlying physical system.
Akil Narayan 0001, Claude Jeffrey Gittelson, Dongbin Xiu   +2 more
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STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION

International Journal for Uncertainty Quantification, 2012
Summary: The idea of \(\ell_1\)-minimization is the basis of the widely adopted compressive sensing method for function approximation. In this paper, we extend its application to high-dimensional stochastic collocation methods. To facilitate practical implementation, we employ orthogonal polynomials, particularly Legendre polynomials, as basis ...
Yan, Liang, Guo, Ling, Xiu, Dongbin
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Stochastic Collocation Method for Stochastic Optimal Boundary Control of the Navier–Stokes Equations

Applied Mathematics & Optimization, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wenju Zhao, Max Gunzburger
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