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This can be expensive, especially in multiple space dimensions. We consider the use of the simple smoothness indicator <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\sigma ^{\\textrm{S}}= \\frac{1}{N_{\\textrm{S}}-1}\\sum _{j} ({\\bar{u}}_{j} - {\\bar{u}}_{m})^2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mi>\u03c3<\/mml:mi>\n                      <mml:mtext>S<\/mml:mtext>\n                    <\/mml:msup>\n                    <mml:mo>=<\/mml:mo>\n                    <mml:mfrac>\n                      <mml:mn>1<\/mml:mn>\n                      <mml:mrow>\n                        <mml:msub>\n                          <mml:mi>N<\/mml:mi>\n                          <mml:mtext>S<\/mml:mtext>\n                        <\/mml:msub>\n                        <mml:mo>-<\/mml:mo>\n                        <mml:mn>1<\/mml:mn>\n                      <\/mml:mrow>\n                    <\/mml:mfrac>\n                    <mml:msub>\n                      <mml:mo>\u2211<\/mml:mo>\n                      <mml:mi>j<\/mml:mi>\n                    <\/mml:msub>\n                    <mml:msup>\n                      <mml:mrow>\n                        <mml:mo>(<\/mml:mo>\n                        <mml:msub>\n                          <mml:mover>\n                            <mml:mrow>\n                              <mml:mi>u<\/mml:mi>\n                            <\/mml:mrow>\n                            <mml:mrow>\n                              <mml:mo>\u00af<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:mover>\n                          <mml:mi>j<\/mml:mi>\n                        <\/mml:msub>\n                        <mml:mo>-<\/mml:mo>\n                        <mml:msub>\n                          <mml:mover>\n                            <mml:mrow>\n                              <mml:mi>u<\/mml:mi>\n                            <\/mml:mrow>\n                            <mml:mrow>\n                              <mml:mo>\u00af<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:mover>\n                          <mml:mi>m<\/mml:mi>\n                        <\/mml:msub>\n                        <mml:mo>)<\/mml:mo>\n                      <\/mml:mrow>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, where <jats:inline-formula><jats:alternatives><jats:tex-math>$$N_{\\textrm{S}}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>N<\/mml:mi>\n                    <mml:mtext>S<\/mml:mtext>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the number of mesh elements in the stencil, <jats:inline-formula><jats:alternatives><jats:tex-math>$${\\bar{u}}_j$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mover>\n                      <mml:mrow>\n                        <mml:mi>u<\/mml:mi>\n                      <\/mml:mrow>\n                      <mml:mrow>\n                        <mml:mo>\u00af<\/mml:mo>\n                      <\/mml:mrow>\n                    <\/mml:mover>\n                    <mml:mi>j<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is the local function average over mesh element <jats:italic>j<\/jats:italic>, and index <jats:italic>m<\/jats:italic> gives the target element. Reconstructions utilizing standard WENO weighting fail with this smoothness indicator. We develop a modification of WENO-Z weighting that gives a reliable and accurate reconstruction of adaptive order, which we denote as SWENOZ-AO. We prove that it attains the order of accuracy of the large stencil polynomial approximation when the solution is smooth, and drops to the order of the small stencil polynomial approximations when there is a jump discontinuity in the solution. Numerical examples in one and two space dimensions on general meshes verify the approximation properties of the reconstruction. They also show it to be about 10 times faster in two space dimensions than reconstructions using the classic smoothness indicator. The new reconstruction is applied to define finite volume schemes to approximate the solution of hyperbolic conservation laws. Numerical tests show results of the same quality as standard WENO schemes using the classic smoothness indicator, but with an overall speedup in the computation time of about\u00a03.5\u20135 times in 2D tests. Moreover, the computational efficiency (CPU time versus error) is noticeably improved.\n<\/jats:p>","DOI":"10.1007\/s10915-023-02319-x","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:02:13Z","timestamp":1692871333000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multidimensional WENO-AO Reconstructions Using a Simplified Smoothness Indicator and Applications to Conservation Laws"],"prefix":"10.1007","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8209-9371","authenticated-orcid":false,"given":"Chieh-Sen","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-5478","authenticated-orcid":false,"given":"Todd","family":"Arbogast","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyu","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"issue":"6","key":"2319_CR1","doi-asserted-by":"publisher","first-page":"3251","DOI":"10.1137\/100792573","volume":"32","author":"T Aboiyar","year":"2010","unstructured":"Aboiyar, T., Georgoulis, E.H., Iske, A.: Adaptive ADER methods using kernel-based polyharmonic spline WENO reconstruction. 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