In this article we propose an adaptive algorithm for the solution of time-dependent boundary value problems (BVPs). To solve numerically these problems, we consider the kernel- based method of lines that allows us to split the spatial and time derivatives, dealing with each separately. This adaptive scheme is based on a leave-one-out cross validation (LOOCV) procedure, which is employed as an error indicator. By this technique, we can first detect the domain areas where the error is estimated to be too large - generally due to steep variations or quick changes in the solution - and then accordingly enhance the numerical solution by applying a two-point refinement strategy. Numerical experiments show the efficacy and performance of our adaptive refinement method. (C) 2022 Elsevier Inc. All rights reserved.

Adaptive LOOCV-based kernel methods for solving time-dependent BVPs

Cavoretto, R
First
2022-01-01

Abstract

In this article we propose an adaptive algorithm for the solution of time-dependent boundary value problems (BVPs). To solve numerically these problems, we consider the kernel- based method of lines that allows us to split the spatial and time derivatives, dealing with each separately. This adaptive scheme is based on a leave-one-out cross validation (LOOCV) procedure, which is employed as an error indicator. By this technique, we can first detect the domain areas where the error is estimated to be too large - generally due to steep variations or quick changes in the solution - and then accordingly enhance the numerical solution by applying a two-point refinement strategy. Numerical experiments show the efficacy and performance of our adaptive refinement method. (C) 2022 Elsevier Inc. All rights reserved.
2022
429
127228
127228
https://arxiv.org/pdf/2203.14091.pdf
Meshless approximation; Kernel methods; Adaptive algorithms; Refinement schemes; Partial differential equations
Cavoretto, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1871172
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