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On Quasi-Norm Interpolation Error Estimation And A Posteriori Error Estimates for p-Laplacian
SIAM Journal on Numerical Analysis, 2002The paper is devoted to the finite element approximation of the \(p\)-Laplacian with zero Dirichlet data. The authors establish a series of interpolation error estimates for several widely used averaging interpolators in some quasi-norms. These estimates are among the key ingredients in their improved a posteriori error analysis for the \(p\)-Laplacian.
Wenbin Liu, Ningning Yan
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Generalization error of ensemble estimators
Proceedings of International Conference on Neural Networks (ICNN'96), 2002It has been empirically shown that a better estimate with less generalization error can be obtained by averaging outputs of multiple estimators. This paper presents an analytical result for the generalization error of ensemble estimators. First, we derive a general expression of the ensemble generalization error by using factors of interest (bias ...
Naonori Ueda, Ryohei Nakano
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Bounds and error estimates for radiosity
Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94, 1994We present a method for determining a posteriori bounds and estimates for local and total errors in radiosity solutions. The ability to obtain bounds and estimates for the total error is crucial fro reliably judging the acceptability of a solution. Realistic estimates of the local error improve the efficiency of adaptive radiosity algorithms, such as ...
Dani Lischinski +2 more
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2003
In this chapter we assume the spacetime K is foliated by a double null canonical foliation that satisfies the assumptions $$O \leqslant \epsilon_0 ,\,D \leqslant \epsilon_0 ,$$ (6.0.1) and we make use of the inequality proved in Theorem M7 $$R \leqslant cQ_K^{\frac{1} {2}} .$$ (6.0.2)
Sergiu Klainerman, Francesco Nicolò
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In this chapter we assume the spacetime K is foliated by a double null canonical foliation that satisfies the assumptions $$O \leqslant \epsilon_0 ,\,D \leqslant \epsilon_0 ,$$ (6.0.1) and we make use of the inequality proved in Theorem M7 $$R \leqslant cQ_K^{\frac{1} {2}} .$$ (6.0.2)
Sergiu Klainerman, Francesco Nicolò
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Guaranteed a-posteriori error estimation and a-posteriori estimation of the pollution error
2001Abstract In Chapter 5 we discussed various estimators for the energy norm of the error in the finite element solution. We discussed the Neumann element residual estimator, the subdo-main residual estimator, the explicit residual estimator, the recovery estimators (e.g. the ZZ–SPR estimator, etc), and analyzed them in two ways:
Ivo Babuška, Theofanis Strouboulis
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1998
In the conceptual idea described in Chapter 6.1, it was assumed that the mean of the sample would deviate from that of the population from which it was collected. This therefore raises the question of how “precisely” does the mean value of the sample reflect that of the population. In other words, how large is the uncertainty of the mean value, or what
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In the conceptual idea described in Chapter 6.1, it was assumed that the mean of the sample would deviate from that of the population from which it was collected. This therefore raises the question of how “precisely” does the mean value of the sample reflect that of the population. In other words, how large is the uncertainty of the mean value, or what
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Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method
IEEE Transactions on Smart Grid, 2021Xiangyu Kong, Xiangyu Kong, Ning Lu
exaly
Errors, Recovery Processes, and Error Estimates
2005O.C. Zienkiewicz, R.L. Taylor, J.Z. Zhu
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