A least-squares Galerkin approach to gradient and Hessian recovery for nondivergence-form elliptic equations
نویسندگان
چکیده
Abstract We propose a least-squares method involving the recovery of gradient and possibly Hessian for elliptic equation in nondivergence form. As our approach is based on Lax–Milgram theorem with curl-free constraint built into target (or cost) functional, discrete spaces require no inf-sup stabilization. show that standard conforming finite elements can be used yielding priori posteriori convergence results. illustrate findings numerical experiments uniform or adaptive mesh refinement.
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ژورنال
عنوان ژورنال: Ima Journal of Numerical Analysis
سال: 2021
ISSN: ['1464-3642', '0272-4979']
DOI: https://doi.org/10.1093/imanum/drab034