scholarly journals Auto tuning SIR model parameters using genetic algorithm

2021 ◽  
Author(s):  
Fei Liu ◽  
Kristen Lee
Keyword(s):  
United States ◽  
Genetic Algorithm ◽  
Observational Data ◽  
Parameter Tuning ◽  
Sir Model ◽  
Model Parameters ◽  
Manual Intervention ◽  
Tuning Process ◽  
Optimal Set ◽  
Good Agreement

Earlier studies comparing Covid-19 simulations using extended SIR model with observed new cases in New Jersey and United States showed good agreement between simulated results and observational data. The parameters of the SIR model controlling the behavior of the model have to be manually adjusted until the modeled results and observations reach good agreement. The parameter tuning process is tedious and time consuming. In this work, we have developed an approach using genetic algorithm to automatically select the most optimal set of parameters to minimize the residual between simulated result and observational data. The parameter tuning process applying SIR model can now be automated without tedious and time consuming manual intervention.

Hot Deformation Behavior of 20CrMnTiH Steel Studied by Johnson-Cook Model and Genetic Algorithm

2014 ◽  
Vol 1035 ◽  
pp. 225-230
Author(s):  
Rong Xia Chai ◽  
Wei Guo ◽  
Chen Guo
Keyword(s):  
Genetic Algorithm ◽  
Hot Deformation ◽  
True Strain ◽  
Model Material ◽  
Model Parameters ◽  
Strain Rates ◽  
Compression Tests ◽  
Deformation Behaviors ◽  
Good Agreement ◽  
Cook Model

Hot compression tests of 20CrMnTiH steel are carried out in the strain rates range from 0.01s-1 to 10s-1 and in the temperature range from 973K to 1123K. The flow behaviors of 20CrMnTiH steel are described based on the analysis of true stress-true strain curves. The flow stress increases with the increasing of strain rate and the decreasing of deforming temperature. Johnson-Cook (J-C) model are used to analyze the hot deformation behaviors. In the constitutive model, material constants are determined based upon the experimental data. Genetic algorithm (GA) is proposed with the aim of optimizing the J-C model parameters. Good agreement is acquired by comparing of the experimental results with predicted results. It validates the efficiency of Johnson-Cook model in describing the material constitutive behavior.

Modeling Heart Rate Dynamics in Response to Changes in Exercise Intensity

2015 ◽  
Author(s):  
Michael J. Mazzoleni ◽  
Claudio L. Battaglini ◽  
Brian P. Mann
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Heart Rate ◽  
Heart Rate Response ◽  
Synthetic Data ◽  
Model Parameters ◽  
Nonlinear Mathematical Model ◽  
Heart Rate Dynamics ◽  
Parameter Values ◽  
Good Agreement

This paper develops a nonlinear mathematical model to describe the heart rate response of an individual during cycling. The model is able to account for the fluctuations of an individual’s heart rate while they participate in exercise that varies in intensity. A method for estimating the model parameters using a genetic algorithm is presented and implemented, and the results show good agreement between the actual parameter values and the estimated values when tested using synthetic data.

scholarly journals Time fused coefficient SIR model with application to COVID-19 epidemic in the United States

2021 ◽  
pp. 1-15
Author(s):  
Hou-Cheng Yang ◽  
Yishu Xue ◽  
Yuqing Pan ◽  
Qingyang Liu ◽  
Guanyu Hu
Keyword(s):  
United States ◽  
The United States ◽  
Sir Model

scholarly journals Optimising top-quark threshold scan at CLIC using genetic algorithm

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
K. Nowak ◽  
A.F. Żarnecki
Keyword(s):  
Genetic Algorithm ◽  
Pair Production ◽  
Top Quark ◽  
Quark Mass ◽  
Mass Measurement ◽  
Production Cross ◽  
Model Parameters ◽  
Top Quark Mass ◽  
Systematic Uncertainties ◽  
Pair Production Cross Section

Abstract One of the important goals at the future e+e− colliders is to measure the top-quark mass and width in a scan of the pair production threshold. However, the shape of the pair-production cross section at the threshold depends also on other model parameters, as the top Yukawa coupling, and the measurement is a subject to many systematic uncertainties. Presented in this work is the study of the top-quark mass determination from the threshold scan at CLIC. The most general approach is used with all relevant model parameters and selected systematic uncertainties included in the fit procedure. Expected constraints from other measurements are also taken into account. It is demonstrated that the top-quark mass can be extracted with precision of the order of 30 to 40 MeV, including considered systematic uncertainties, already for 100 fb−1 of data collected at the threshold. Additional improvement is possible, if the running scenario is optimised. With the optimisation procedure based on the genetic algorithm the statistical uncertainty of the mass measurement can be reduced by about 20%. Influence of the collider luminosity spectra on the expected precision of the measurement is also studied.

Investigating the vibrational behaviour of a rotating two-blade propeller by using a self-tracking method

2018 ◽  
Vol 233 (3) ◽  
pp. 835-847 ◽  
Author(s):  
Mohammad-Reza Ashory ◽  
Farhad Talebi ◽  
Heydar R Ghadikolaei ◽  
Morad Karimpour
Keyword(s):  
Natural Frequency ◽  
Measurement Errors ◽  
Mode Shapes ◽  
Model Parameters ◽  
Test Results ◽  
Modal Assurance Criterion ◽  
Tracking Method ◽  
Strong Resemblance ◽  
Test Scenarios ◽  
Good Agreement

This study investigated the vibrational behaviour of a rotating two-blade propeller at different rotational speeds by using self-tracking laser Doppler vibrometry. Given that a self-tracking method necessitates the accurate adjustment of test setups to reduce measurement errors, a test table with sufficient rigidity was designed and built to enable the adjustment and repair of test components. The results of the self-tracking test on the rotating propeller indicated an increase in natural frequency and a decrease in the amplitude of normalized mode shapes as rotational speed increases. To assess the test results, a numerical model created in ABAQUS was used. The model parameters were tuned in such a way that the natural frequency and associated mode shapes were in good agreement with those derived using a hammer test on a stationary propeller. The mode shapes obtained from the hammer test and the numerical (ABAQUS) modelling were compared using the modal assurance criterion. The examination indicated a strong resemblance between the hammer test results and the numerical findings. Hence, the model can be employed to determine the other mechanical properties of two-blade propellers in test scenarios.

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Field Data ◽  
Back Propagation ◽  
Earth Dam ◽  
Earth Dams ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Good Agreement

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

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