Sentence examples for model calibration algorithm from inspiring English sources

This work proposes a surrogate based model calibration algorithm.

The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation.

In this work, a scalable algorithm for model calibration in nuclear engineering applications is presented and tested.

Two main approaches of district-wide energy model calibration are proposed using a genetic algorithm and compared to a baseline case where UHI is not considered.

The genetic evolutionary algorithms used for model calibration had the following settings: the maximum number of generations, as well as the number of individuals in each generation, was set to 30 based on our experience with the optimiser and as a compromise between performance (optimisation time) and target precision.

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection.

The steps of the original calibration algorithm are as follows: Fit the model using the probit-space random-effects likelihood for ANC sites via incremental mixture importance sampling.

The definition of training data was realised on Kennard-Stone sampling algorithm and we selected 103 samples for model calibration and 100 samples for model validation.

In order to systematically search the parameter space for model calibration in a reproducible manner, we used the Simulated Annealing (SA) algorithm, a commonly used engineering optimization technique [33].

Essentially, all approaches to this problem, which is often referred to as model calibration, are based on deriving a cost function and choosing an optimization algorithm to minimize that function [ 10].

The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been shown to be an effective and efficient MOGA calibration algorithm for a wide variety of applications including for SWAT model calibration.