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A priori error estimation for the SOREL mission
Acta Astronautica, 1975Abstract In order to estimate the accuracy to which the post-Newtonian relativistic parameters β, γ of the Eddington-Robertson metric and the solar oblateness J 2 can be obtained by using a drag-free space probe (SOREL), a sophisticated mathematical model has been developed. This model includes all relevant perturbations of the motion and accounts
E. A. Roth
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A priori error estimates for hp penalty BEM for contact problems in elasticity
Computer Methods in Applied Mechanics and Engineering, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. Chernov, M. Maischak, E. Stephan
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A Priori Error Estimates for Nonlinear Scalar Conservation Laws
, 1999In this note, we review the recent work of the authors on a priori error estimates for nonlinear scalar conservation laws. A priori error estimates are important because they shed light into the nature of the corresponding numerical scheme. In this note, we show how to use our a priori error estimation technique to study multidimensional monotone ...
Bernardo Cockburn, J. Yang, P. Gremaud
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Constructive A Priori Error Estimates for a Full Discrete Approximation of the Heat Equation
SIAM Journal on Numerical Analysis, 2013M. Nakao, Takuma Kimura, T. Kinoshita
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A Priori Inverse Operator Estimation for Guaranteed Error Estimate
Proceedings of the 4th International Workshop on Reliable Engineering Computing Robust Design – Coping with Hazards, Risk and Uncertainty – REC 2010, 2010A guaranteed error estimate procedure for linear or nonlinear two-point boundary value problems is established by authors. ‘Guaranteed’ error estimate is rigorous, i.e. it takes into account every error such as the discretization error and the rounding error when we compute an approximate solution.
Kubo T., Oishi S., Takayasu A.
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A Priori Estimates of the Generalization Error for Autoencoders
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020Autoencoder is a machine learning model which aims for dimensionality reduction, by reconstructing its input through a bottleneck with lower dimension than the input. It is among the most popular models used in unsupervised learning and semi-supervised learning. In this paper, we build theoretical understanding about autoencoders.
Zehao Don, Weinan E., Chao Ma
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A Priori Error Bound for Moment Matching Approximants of Thermal Models
IEEE Transactions on Components, Packaging, and Manufacturing Technology, 2019This article focuses on the moment matching methodology for deriving dynamic compact thermal models (DCTMs) of electronic components. For the first time, an a priori error bound is theoretically established both in the time and frequency domains, which ...
L. Codecasa +2 more
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Unified a Priori Error Estimate and a Posteriori Error Estimate of CIP-FEM for Elliptic Equations
Advances in Applied Mathematics and Mechanics, 2016AbstractThis paper is devoted to a unified a priori and a posteriori error analysis of CIP-FEM (continuous interior penalty finite element method) for second-order elliptic problems. Compared with the classic a priori error analysis in literature, our technique can easily apply for any type regularity assumption on the exact solution, especially for ...
Wang, Jianye, Ma, Rui
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