### Table 3.1 Parameter: linear heat flow model

### Table 3.2 Parameter: perturbed linear heat flow model

### Table 5.3: Number of two-grid V-cycles for the 2D entering ow problem using inexact, exact, and Galerkin coarse grid corrections. We next consider the model entering ow and recirculating ow problems in two dimensions (cf. Section 4). Similar multigrid setting as in the one-dimensional case: Euler apos;s smoothing, linear interpolation and full weighting restriction. Since the CFL number = 0:25 in two dimension, we use 2 presmoothing and postsmoothing steps

### Table 5: Relative Error Norms of Heat Model

2001

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### Table 3 Heat Wave Model Estimates

"... In PAGE 15: ...We proceed to examine the per-hour volatility of yield changes within each trading center. The estimated GARCH(1,1) results, shown in Table3 , indicate the presence of the GARCH form of heteroskedasticity in intraday yields. The GARCH parameters and the Wald statistics with respect to the homoskedastic model are significant at the 5% level for all three series.... ..."

### Table 3: Relative error norms of the heat model

2000

Cited by 1

### Table 2: Heat ux parameters

"... In PAGE 6: ... In this example, a sinh plastic strain evolution model is used as in [5]: _ ~ p = f(~ ; s; ) = A exp( C + 273)[sinh B~ ]n (10) Constitutive parameters for the present calculation are given in [5] and parameters related with the heat transfer conditions at the mold/metal shell interface (Eqs. 1 and 2) are given in Table2 . The amplitude of the mold topography A is selected to be 0:232 mm.... ..."

### Table 2 - Radiative Heat Transfer at Stagnation

"... In PAGE 7: ...85 W=cm 2 as opposed to the 53.58 W=cm 2 for the baseline model #28 Table2 #29. Table 2 - Radiative Heat Transfer at Stagnation... ..."

### Table 1. The Calculated Results for Analyzed Data-Set

2000

"... In PAGE 9: ... In order to have easy interpretable models, we have fixed the maximal number of terms in the equation to be equal to 8 and the maximum degree of polynoms to be equal to 3. The calculations performed using the select params option of the ANALYSIS are summarized in Table1 . The number of stored models was 3.... In PAGE 9: ... It was shown that the use of significant variables, as detected by MUSEUM, = improved PLS results (compare data in column 7 vs. column 6 in Table1 ). The similar tendency was also observed if only variables found to be relevant by the PNN algorithm were used in the cross-validation calculations (compare the last and 7 columns of Table 1).... In PAGE 12: ... b Number of significant PLS components. c The cross-validated q2 calculated using input variables optimized by MUSEUM approach (unless not stated otherwise the PLS results are from Table1 and 15 of (2)). d Number of input variables selected by PNN.... ..."

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