A Study on Stable Regularized Moving Least-Squares Interpolation and Coupled with SPH Method

<div>The comparison of the RMSE with different kernel functions (left) and the SRMLS relative error contours with renormalized Gauss kernel function and linear basis (right): (a) errors of function value; (b) errors of <span class="nowrap"><svg height="12.7178pt" id="M66" style="vertical-align:-3.42947pt" version="1.1" viewbox="-0.0498162 -9.28833 32.343 12.7178" width="32.343pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M471 379C471 433 467 511 427 578C379 658 305 712 208 712C154 712 98 699 70 684C70 668 95 634 111 634C141 651 180 665 234 665C306 665 350 627 372 575C391 530 393 480 393 442V415C371 436 344 448 313 448C280 448 227 437 176 411C100 372 23 279 23 148C23 67 64 -12 180 -12C293 -12 360 46 407 129C450 205 471 310 471 379ZM382 335C382 142 289 50 238 35C226 31 213 28 200 28C139 28 110 91 110 164C110 310 191 382 223 395C245 404 259 409 280 409C305 409 332 404 359 388C379 376 382 362 382 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Mathematical Problems in Engineering

fig7

Figure 7

Figure 7: A Study on Stable Regularized Moving Least-Squares Interpolation and Coupled with SPH Method 