scholarly journals Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Roussel ◽  
Juan Pablo Gonzalez-Aguilera ◽  
Young-Kee Kim ◽  
Eric Wisniewski ◽  
Wanming Liu ◽  
...  
Keyword(s):  
Parameter Space ◽  
Prior Information ◽  
Input Parameter ◽  
Bayesian Optimization ◽  
Particle Accelerators ◽  
Single Shot ◽  
Physical Sciences ◽  
Bayesian Optimization Algorithm ◽  
Scientific Disciplines ◽  
Input Parameters

AbstractParticle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces  the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

Linkage Problem, Distribution Estimation, and Bayesian Networks

2000 ◽  
Vol 8 (3) ◽  
pp. 311-340 ◽  
Author(s):  
Martin Pelikan ◽  
David E. Goldberg ◽  
Erick Cantú-Paz
Keyword(s):  
Bayesian Networks ◽  
Joint Distribution ◽  
Prior Information ◽  
Linear Time ◽  
Building Blocks ◽  
Bayesian Optimization ◽  
Problem Size ◽  
Bayesian Optimization Algorithm ◽  
Domain Information ◽  
Estimation Of Distributions

This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. The algorithm is settled into the context of genetic and evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian Optimization Algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The BOA identifies, reproduces, and mixes building blocks up to a specified order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm, but it is not essential. First experiments were done with additively decomposable problems with both nonoverlapping as well as overlapping building blocks. The proposed algorithm is able to solve all but one of the tested problems in linear or close to linear time with respect to the problem size. Except for the maximal order of interactions to be covered, the algorithm does not use any prior knowledge about the problem. The BOA represents a step toward alleviating the problem of identifying and mixing building blocks correctly to obtain good solutions for problems with very limited domain information.

scholarly journals Sensitivity analysis of an electrophysiology model for the left ventricle

2020 ◽  
Vol 17 (171) ◽  
pp. 20200532
Author(s):  
Giulio Del Corso ◽  
Roberto Verzicco ◽  
Francesco Viola
Keyword(s):  
Sensitivity Analysis ◽  
Left Ventricle ◽  
Parameter Space ◽  
Cardiac Electrophysiology ◽  
Input Parameter ◽  
Medical Knowledge ◽  
Training Dataset ◽  
Quasi Monte Carlo ◽  
Input Parameters ◽  
Using Data

Modelling the cardiac electrophysiology entails dealing with the uncertainties related to the input parameters such as the heart geometry and the electrical conductivities of the tissues, thus calling for an uncertainty quantification (UQ) of the results. Since the chambers of the heart have different shapes and tissues, in order to make the problem affordable, here we focus on the left ventricle with the aim of identifying which of the uncertain inputs mostly affect its electrophysiology. In a first phase, the uncertainty of the input parameters is evaluated using data available from the literature and the output quantities of interest (QoIs) of the problem are defined. According to the polynomial chaos expansion, a training dataset is then created by sampling the parameter space using a quasi-Monte Carlo method whereas a smaller independent dataset is used for the validation of the resulting metamodel. The latter is exploited to run a global sensitivity analysis with nonlinear variance-based indices and thus reduce the input parameter space accordingly. Thereafter, the uncertainty probability distribution of the QoIs are evaluated using a direct UQ strategy on a larger dataset and the results discussed in the light of the medical knowledge.

An improved 2-pentanone low to high-temperature kinetic model using Bayesian Optimization algorithm

2021 ◽  
Vol 231 ◽  
pp. 111453
Author(s):  
Qianjin Lin ◽  
Chun Zou ◽  
Shibo Liu ◽  
Yunpeng Wang ◽  
Lixin Lu ◽  
...  
Keyword(s):  
High Temperature ◽  
Kinetic Model ◽  
Optimization Algorithm ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Gaussian Process Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

2021 ◽  
Author(s):  
Arunabha Batabyal ◽  
Sugrim Sagar ◽  
Jian Zhang ◽  
Tejesh Dube ◽  
Xuehui Yang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Phase Field ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Neck Size ◽  
Sources Of Uncertainty ◽  
Surface Diffusivity ◽  
Input Parameters ◽  
Optimal Values ◽  
Probability Of Improvement

Abstract A persistent problem in the selective laser sintering process is to maintain the quality of additively manufactured parts, which can be attributed to the various sources of uncertainty. In this work, a two-particle phase-field microstructure model has been analyzed. The sources of uncertainty as the two input parameters were surface diffusivity and inter-particle distance. The response quantity of interest (QOI) was selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005, respectively, while those from Expected Improvement were 23.9893 and 33.9627, respectively. The optimization results from the two different acquisition functions seemed to be in good agreement.

scholarly journals A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

2022 ◽  
Vol 12 (1) ◽  
pp. 55
Author(s):  
Fatih Demir ◽  
Kamran Siddique ◽  
Mohammed Alswaitti ◽  
Kursat Demir ◽  
Abdulkadir Sengur
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Bayesian Optimization ◽  
L1 Norm ◽  
Bayesian Optimization Algorithm ◽  
Knn Classifier ◽  
Multi Level

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

scholarly journals CONCEPT OF ECONOMICAL‐MATHEMATICAL MODEL OF LAND MARKET

2007 ◽  
Vol 13 (4) ◽  
pp. 333-340
Author(s):  
Gintautas Šatkauskas
Keyword(s):  
Mathematical Model ◽  
Input Parameter ◽  
Market Price ◽  
Research Process ◽  
Land Market ◽  
Market Prices ◽  
Noise Background ◽  
Input Parameters ◽  
Main Components ◽  
Sufficient Precision

Input parameters, ie factors defining the market price of agricultural‐purpose land, are interrelated very often by means of non‐linear ties. Strength of these ties is rather different and this limits usefulness of information in the research process of land market prices. Influence of input parameter changes to the input parameters in case when there are rather substantial changes may be determined in someone direction with a sufficient precision, whereas in other directions with comparatively small changes of input parameters this influence is difficult to be separated from the “noise” background. Taking into account the above‐listed circumstances, the concept of economical‐mathematical model of land market should be as follows: there is carried out re‐parameterisation of the process by means of introduction of new parameters in such a way that the new parameters are not interrelated, and the full process is evaluated at the minimal number of these parameters. These requirements are met by the main components of the input parameters. Then normalisation of the main components is carried out and dependencies on new parameters are determined. It is easier to interpret the dependencies obtained having reduced the number of input parameters and the higher the non‐linearity of interrelations of primary land market data, the greater effect of normalisation of input-parameter components. The results are compared with the valuations of experts.

scholarly journals BAYESIAN OPTIMIZATION OF CRYSTALLIZATION PROCESSES TO GUARANTEE END-USE PRODUCT PROPERTIES

2020 ◽  
Vol 50 (2) ◽  
pp. 109-114
Author(s):  
Martin Francisco Luna ◽  
Ernesto Carlos Martinez
Keyword(s):  
Crystal Size ◽  
Process Development ◽  
Operating Conditions ◽  
Bayesian Optimization ◽  
Dissolution Profile ◽  
Bayesian Optimization Algorithm ◽  
Probability Of Success ◽  
End Use ◽  
One Class Classification ◽  
Experimental Runs

For pharmaceutical solid products, the issue of reproducibly obtaining their desired end-use properties depending on crystal size and form is the main problem to be addressed and solved in process development. Lacking a reliable first-principles model of a crystallization process, a Bayesian optimization algorithm is proposed. On this basis, a short sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization can take advantage of the full information provided by the sequence of experiments made using a probabilistic model of the probability of success based on a one-class classification method. The proposed algorithm’s performance is tested in silico using the crystallization and formulation of an API product where success is about fulfilling a dissolution profile as required by the FDA. Results obtained demonstrate that the sequence of generated experiments allows pinpointing operating conditions for reproducible quality.

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