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It generally involves non local error contributions.
Local error recovery in SRM: comparison of two approaches.
These methods enable evaluation of the local error.
Defect integrals or local truncation errors are used as local error quantities.
The design makes estimation of local error and interpolation of the solution possible without additional stages.
We avoid special neural net architectures and use standard regression procedures to determine local error bars.
Instead of step-size control based on local error evaluation, an optimal integration method is selected.
Integration while meeting the required local error proceeds with constant-sized steps.
Under local error bounded assumption, the method is superlinear and quadratic convergent on F x).
A residual-based error estimator is used as local error indicator during the h-adaptive procedures.
The new method is compared with several strategies using local error estimators or recovered Hessians.
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