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Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Journal of Magnesium and Alloys 2024年第2期12卷 750-766页

作者： Xiaoxi Mi Lili Dai Xuerui Jing Jia She Bjørn Holmedal Aitao Tang Fusheng Pan College of Materials Science and Engineering Chongqing UniversityChongqing 400044China Southwest Technology and Engineering Research Institute Chongqing 400039China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Department of Materials Science and Engineering Norwegian University of Science and Technology(NTNU)Trondheim 7051Norway

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

关键词： Mg-Mn-based alloys High-performance Alloy design Machine learning bayesian optimization

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bayesian optimization+XGBoost based life cycle carbon emission prediction for residential buildings—An example from Chengdu,China

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建筑模拟(英文) 2023年第8期16卷 1451-1466页

作者： Haize Pan Chengjin Wu School of Civil Engineering and Geomatics Southwest Petroleum UniversityChengdu 610500China

The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China’s carbon reduction efforts.The building design phase has the most significant potential to reduce building life-cycle carbon emissions(LCCO_(2)).However,the lack of detailed inventory data at the design stage makes calculating a building’s LCCO_(2) very difficult and complex.Therefore,accurate prediction of building LCCO_(2) at the design stage using relevant design factors is essential to reduce carbon emissions.This paper proposes an ensemble learning algorithm combining bayesian optimization and extreme gradient boosting(BO-XGBoost)to predict LCCO_(2) accurately in residential buildings.First,this study collected and calculated the LCCO_(2) of 121 residential buildings in Chengdu,China.Second,a carbon emission prediction model was developed using XGBoost based on 15 design factors,and hyperparameter optimization was performed using the BO algorithm.Finally,the model performance was evaluated using two evaluation metrics,coefficient of determination(R2)and root mean square error(RMSE),and the prediction performance of other models was compared with that of the BO-XGBoost model.The results show that the RMSE of the proposed BO-XGBoost for predicting LCCO_(2) in residential buildings is at least 40%lower compared to other models.The method adopted in this study can help designers accurately predict building LCCO_(2) at the early design stage and provide methodological support for similar studies in the future.

关键词： building carbon emissions life cycle XGBoost bayesian optimization predictive model

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bayesian optimization for Field-Scale Geological Carbon Storage

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Engineering 2022年第11期18卷 96-104页

作者： Xueying Lu Kirk E.Jordan Mary F.Wheeler Edward O.Pyzer-Knapp Matthew Benatan Center for Subsurface Modeling Oden Institute for Computational Engineering and SciencesThe University of Texas at AustinAustinTX 78712USA IBM Research United Kingdom Warrington WA44ADUK

We present a framework that couples a high-fidelity compositional reservoir simulator with bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage.The implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during CO_(2)sequestration.IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations.In this work,we use the compositional flow module to simulate the geological carbon storage process.The compositional flow model,which includes a hysteretic three-phase relative permeability model,accounts for three major CO_(2)trapping mechanisms:structural trapping,residual gas trapping,and solubility trapping.Furthermore,IPARS is coupled to the International Business Machines(IBM)Corporation bayesian optimization Accelerator(BOA)for parallel optimizations of CO_(2)injection strategies during field-scale CO_(2)sequestration.BO builds a probabilistic surrogate for the objective function using a bayesian machine learning algorithm-the Gaussian process regression,and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample.The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel(batch)executions,scales better for high-dimensional problems,and is more robust to initializations.We demonstrate these merits by applying the algorithm in the optimization of the CO_(2)injection schedule in the Cranfield site in Mississippi,USA,using field data.The optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the baseline.The performance of BO is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-evolution strategy(ES).The results demonstrate th

关键词： Compositional flow bayesian optimization Geological carbon storage CCUS Machine learning AI for science

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Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via bayesian optimization machine learning

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International Journal of Minerals,Metallurgy and Materials 2022年第6期29卷 1197-1205页

作者： Hongtao Zhang Huadong Fu Yuheng Shen Jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory for Advanced Materials Processing(MOE) University of Science and Technology BeijingBeijing 100083China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation University of Science and Technology BeijingBeijing 100083China

It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element,in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy,while the properties are as the same level as C70350 alloy.Here we adopted a strategy combining bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters(cold rolling deformation 90%,aging temperature 450℃,and aging time 1.25 h)of the new copper alloy with only 32 experiments(27 basic sample data acquisition experiments and 5 iteration experiments),which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy.The experimental hardness,tensile strength,and electrical conductivity of the new copper alloy are HV(285±4),(872±3)MPa,and(44.2±0.7)%IACS(international annealed copper standard),reaching the property level of the commercial lead frame C70350 alloy.This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.

关键词： copper alloy process design machine learning bayesian optimization utility function

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Bottom hole pressure prediction based on hybrid neural networks and bayesian optimization

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Petroleum Science 2023年第6期20卷 3712-3722页

作者： Chengkai Zhang Rui Zhang Zhaopeng Zhu Xianzhi Song Yinao Su Gensheng Li Liang Han College of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China College of Artificial Intelligence China University of Petroleum(Beijing)Beijing102249China National Key Laboratory of Petroleum Resources and Engineering China University of Petroleum(Beijing)Beijing102249China CNPC Engineering Technology R&D Company Limited Beijing102206China

Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal correlations of measurement while drilling (MWD) data with traditional intelligent models. In this work, we develop a novel hybrid neural network, which integrates the Convolution Neural Network (CNN) and the Gate Recurrent Unit (GRU) for predicting BHP fluctuations more accurately. The CNN structure is used to analyze spatial local dependency patterns and the GRU structure is used to discover depth variation trends of MWD data. To further improve the prediction accuracy, we explore two types of GRU-based structure: skip-GRU and attention-GRU, which can capture more long-term potential periodic correlation in drilling data. Then, the different model structures tuned by the bayesian optimization (BO) algorithm are compared and analyzed. Results indicate that the hybrid models can extract spatial-temporal information of data effectively and predict more accurately than random forests, extreme gradient boosting, back propagation neural network, CNN and GRU. The CNN-attention-GRU model with BO algorithm shows great superiority in prediction accuracy and robustness due to the hybrid network structure and attention mechanism, having the lowest mean absolute percentage error of 0.025%. This study provides a reference for solving the problem of extracting spatial and temporal characteristics and guidance for managed pressure drilling in complex formations.

关键词： Bottom hole pressure Spatial-temporal information Improved GRU Hybrid neural networks bayesian optimization

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Dendritic Cell Algorithm with bayesian optimization Hyperband for Signal Fusion

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Computers, Materials & Continua 2023年第8期76卷 2317-2336页

作者： Dan Zhang Yu Zhang Yiwen Liang School of Computer Science Wuhan UniversityWuhan430072China GNSS Research Center Wuhan UniversityWuhan430079China

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

关键词： Dendritic cell algorithm signal fusion parameter optimization bayesian optimization hyperband

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Intelligent rockburst prediction model with sample category balance using feedforward neural network and bayesian optimization

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Underground Space 2022年第5期7卷 833-846页

作者： Diyuan Li Zida Liu Peng Xiao Jian Zhou Danial Jahed Armaghani School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,bayesian optimization and the synthetic minority oversampling technique+Tomek Link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%).

关键词： Rockburst prediction Feedforward neural network bayesian optimization SMOTETomek

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Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-bayesian optimization

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Computers, Materials & Continua 2023年第5期75卷 3223-3238页

作者： Alawi Alqushaibi Mohd Hilmi Hasan Said Jadid Abdulkadir Amgad Muneer Mohammed Gamal Qasem Al-Tashi Shakirah Mohd Taib Hitham Alhussian Computer&Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Department of Imaging Physics University of Texas MD Anderson CancerHoustonTXUSA

Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the bayesian optimization algorithm.The findings indicate that the bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.

关键词： Type 2 diabetes diabetes mellitus convolutional neural network bayesian optimization SMOTE

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Preparing quantum states by measurement-feedback control with bayesian optimization

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Frontiers of physics 2023年第6期18卷 291-299页

作者： Yadong Wu Juan Yao Pengfei Zhang Department of Physics Fudan UniversityShanghai 200438China State Key Laboratory of Surface Physics Key Laboratory of Micro and Nano Photonic Structures(MOE)Fudan UniversityShanghai 200438China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200433China Shanghai Qi Zhi Institute AI TowerXuhui DistrictShanghai 200232China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Walter Burke Institute for Theoretical Physics&Institute for Quantum Information and Matter California Institute of TechnologyPasadenaCA 91125USA

The preparation of quantum states is crucial for enabling quantum computations and simulations.In this work,we present a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process(MFCP)with machine learning techniques.Specifically,we employ bayesian optimization(BO)to enhance the efficiency of determining the measurement and feedback operators within the MFCP.As an illustration,we study the ground state preparation of the one-dimensional Bose−Hubbard model.Through BO,we are able to identify optimal parameters that can effectively drive the system towards low-energy states with a high probability across various quantum trajectories.Our results open up new directions for further exploration and development of advanced control strategies for quantum computations and simulations.

关键词： state preparation feedback control bayesian optimization

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Breast Cancer Diagnosis Using Feature Selection Approaches and bayesian optimization

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Computer Systems Science & Engineering 2023年第5期45卷 1017-1031页

作者： Erkan Akkur Fuat TURK Osman Erogul Department of Biomedical Engineering TOBB University of Economics and TechnologyAnkara06560Turkey Deparment of Computer Engineering Karatekin UniversityÇankırı18100Turkey

Breast cancer seriously affects many women.If breast cancer is detected at an early stage,it may be cured.This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage.It has been used by combining feature selection and bayesian optimization approaches to build improved machine learning models.Support Vector Machine,K-Nearest Neighbor,Naive Bayes,Ensemble Learning and Decision Tree approaches were used as machine learning algorithms.All experiments were tested on two different datasets,which are Wisconsin Breast Cancer Dataset(WBCD)and Mammographic Breast Cancer Dataset(MBCD).Experiments were implemented to obtain the best classification process.Relief,Least Absolute Shrinkage and Selection Operator(LASSO)and Sequential Forward Selection were used to determine the most relevant features,respectively.The machine learning models were optimized with the help of bayesian optimization approach to obtain optimal hyperparameter values.Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms.Among the various experiments,LASSO-BO-SVM showed the highest accuracy,precision,recall and F1-score for two datasets(97.95%,98.28%,98.28%,98.28%for MBCD and 98.95%,97.17%,100%,98.56%for MBCD),yielding outperforming results compared to recent studies.

关键词： Breast cancer machine learning bayesian optimization feature selection

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