scholarly journals Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

2021 ◽  
Vol 19 ◽  
pp. 41-48
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Monte Carlo Analysis ◽  
Computational Effort ◽  
Benchmark Problem ◽  
Yield Optimization ◽  
Multi Objective ◽  
Electromagnetic Devices ◽  
The Impact

Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

scholarly journals A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

scholarly journals Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators

2019 ◽  
Vol 39 (4) ◽  
pp. 405-413 ◽  
Author(s):  
Tiago M. de Carvalho ◽  
Eveline A. M. Heijnsdijk ◽  
Luc Coffeng ◽  
Harry J. de Koning
Keyword(s):  
Prostate Cancer ◽  
Simulation Model ◽  
Gaussian Process ◽  
Prediction Error ◽  
Life Histories ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Computational Effort ◽  
Sensitivity Analyses ◽  
Model Parameters

Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

scholarly journals Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Majedeh Gheytanzadeh ◽  
Alireza Baghban ◽  
Sajjad Habibzadeh ◽  
Amin Esmaeili ◽  
Otman Abida ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Fossil Fuels ◽  
Global Climate ◽  
Gaussian Process Regression ◽  
Accurate Estimate ◽  
Kernel Functions ◽  
Predictive Tool ◽  
Input Variables ◽  
Capture Techniques ◽  
The Impact

AbstractIn recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

A Gaussian process regression based hybrid approach for short-term wind speed prediction

2016 ◽  
Vol 126 ◽  
pp. 1084-1092 ◽  
Author(s):  
Chi Zhang ◽  
Haikun Wei ◽  
Xin Zhao ◽  
Tianhong Liu ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Chao Wang ◽  
Samantha M. Scott ◽  
Kanagaraj Subramanian ◽  
Salvatore Loguercio ◽  
Pei Zhao ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Membrane Trafficking ◽  
Histone Deacetylases ◽  
Gaussian Process Regression ◽  
Rapid Onset ◽  
Cholesterol Homeostasis ◽  
Spatial Covariance ◽  
Npc1 Gene ◽  
The Individual ◽  
The Impact

Abstract To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual.

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