{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T14:46:37Z","timestamp":1766587597466,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T00:00:00Z","timestamp":1600300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FOINS program of Consejo Nacional de Ciencia y Tecnolog\u00eda (CONACYT),Problemas Nacionales","award":["5241"],"award-info":[{"award-number":["5241"]}]},{"name":"FOINS program of Consejo Nacional de Ciencia y Tecnolog\u00eda (CONACYT),C\u00e1tedras CONACYT","award":["556"],"award-info":[{"award-number":["556"]}]},{"name":"SIP-IPN research grants","award":["SIP 2083","SIP 20200640","SIP 20200811"],"award-info":[{"award-number":["SIP 2083","SIP 20200640","SIP 20200811"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model\u2019s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.<\/jats:p>","DOI":"10.3390\/s20185332","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T07:27:33Z","timestamp":1600414053000},"page":"5332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2734-7560","authenticated-orcid":false,"given":"Carlos A.","family":"Duchanoy","sequence":"first","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"},{"name":"C\u00e1tedra CONACyT, Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2836-2102","authenticated-orcid":false,"given":"Hiram","family":"Calvo","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-9197","authenticated-orcid":false,"given":"Marco A.","family":"Moreno-Armend\u00e1riz","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, Y., Cheng, Q.S., and Koziel, S. (2019). Multi-fidelity local surrogate model for computationally efficient microwave component design optimization. Sensors, 19.","DOI":"10.3390\/s19133023"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qin, S., Zhang, Y., Zhou, Y.L., and Kang, J. (2018). Dynamic model updating for bridge structures using the kriging model and PSO algorithm ensemble with higher vibration modes. Sensors, 18.","DOI":"10.3390\/s18061879"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3182\/20070709-3-RO-4910.00004","article-title":"Iterative feedback and learning control. Servo systems applications","volume":"40","author":"Preitl","year":"2007","journal-title":"IFAC Proc. Vol."},{"key":"ref_4","first-page":"154","article-title":"A machine learning approach to classify pedestrians\u2019 events based on IMU and GPS","volume":"17","author":"Ahmed","year":"2019","journal-title":"Int. J. Artif. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0166-3615(03)00128-3","article-title":"Process analysis and product quality estimation by self-organizing maps with an application to polyethylene production","volume":"52","author":"Abonyi","year":"2003","journal-title":"Comput. Ind."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"167653","DOI":"10.1109\/ACCESS.2019.2953499","article-title":"A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications","volume":"7","author":"Barricelli","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., and D\u2019Amato, E. (2020). Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing. Sensors, 20.","DOI":"10.3390\/s20010097"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, C., Gao, J., Bi, Y., Shi, X., and Tian, D. (2020). A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control. Sensors, 20.","DOI":"10.3390\/s20123515"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1162\/ARTL_a_00225","article-title":"On design mining: Coevolution and surrogate models","volume":"23","author":"Preen","year":"2017","journal-title":"Artif. Life"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/TEVC.2014.2316199","article-title":"Toward the coevolution of novel vertical-axis wind turbines","volume":"19","author":"Preen","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_11","unstructured":"Ong, Y., Keane, A.J., and Nair, P.B. (2002, January 18\u201322). Surrogate-assisted coevolutionary search. Proceedings of the IEEE 9th International Conference on Neural Information Processing, ICONIP\u201902, Singapore."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Goh, C.K., Lim, D., Ma, L., Ong, Y.S., and Dutta, P.S. (2011, January 5\u20138). A surrogate-assisted memetic co-evolutionary algorithm for expensive constrained optimization problems. Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, USA.","DOI":"10.1109\/CEC.2011.5949693"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8553","DOI":"10.1021\/ie501024w","article-title":"Multivariate statistical process control method including soft sensors for both early and accurate fault detection","volume":"53","author":"Masuda","year":"2014","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Luczak, T., Burch V, R.F., Smith, B.K., Carruth, D.W., Lamberth, J., Chander, H., Knight, A., Ball, J., and Prabhu, R. (2020). Closing the wearable gap\u2014Part V: Development of a pressure-sensitive sock utilizing soft sensors. Sensors, 20.","DOI":"10.3390\/s20010208"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, W., Ro, K., Kim, S., and Bae, J. (2017). A soft sensor-based three-dimensional (3-D) finger motion measurement system. Sensors, 17.","DOI":"10.3390\/s17020420"},{"key":"ref_16","unstructured":"Farahani, H.S., Fatehi, A., Shoorehdeli, M.A., and Nadali, A. (2020). A Novel Method For Designing Transferable Soft Sensors And Its Application. arXiv."},{"key":"ref_17","first-page":"75","article-title":"Evaluating the effect of dataset size on predictive model using supervised learning technique","volume":"1","author":"Ajiboye","year":"2015","journal-title":"Int. J. Softw. Eng. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2017). Deep reinforcement learning that matters. arXiv.","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"ref_19","unstructured":"Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J.E., and Stoica, I. (2018). Tune: A research platform for distributed model selection and training. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated Machine Learning: Methods, Systems, Challenges, Springer Nature.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_21","first-page":"77","article-title":"Fitting segmented regression models by grid search","volume":"29","author":"Lerman","year":"1980","journal-title":"J. R. Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Fugara, A., Ahmadlou, M., Al-Shabeeb, A.R., AlAyyash, S., Al-Amoush, H., and Al-Adamat, R. (2020). Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression. Geocarto Int., 1\u201320.","DOI":"10.1080\/10106049.2020.1716396"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Abas, M.A.H., Ismail, N., Ali, N.A., Tajuddin, S., and Tahir, N.M. (2020). Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning. Int. J., 8.","DOI":"10.30534\/ijeter\/2020\/55862020"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, L., Yuan, Z., Wang, Z., Zhao, L., Zhang, Y., Lu, X., and Cao, L. (2020). Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model. Sensors, 20.","DOI":"10.3390\/s20102777"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, P., Zhou, Q., and Shao, X. (2020). Surrogate-Model-Based Design and Optimization. Surrogate Model-Based Engineering Design and Optimization, Springer.","DOI":"10.1007\/978-981-15-0731-1"},{"key":"ref_26","first-page":"311","article-title":"The 2 k\u2014p fractional factorial designs","volume":"3","author":"Box","year":"1961","journal-title":"Technometrics"},{"key":"ref_27","unstructured":"Myers, R.H., Montgomery, D.C., and Anderson-Cook, C.M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/0378-3758(94)00035-T","article-title":"Exploratory designs for computational experiments","volume":"43","author":"Morris","year":"1995","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.2000.10485979","article-title":"A comparison of three methods for selecting values of input variables in the analysis of output from a computer code","volume":"42","author":"McKay","year":"2000","journal-title":"Technometrics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s00158-017-1739-8","article-title":"A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design","volume":"57","author":"Liu","year":"2018","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, H., Xu, S., Ma, Y., Chen, X., and Wang, X. (2016). An adaptive Bayesian sequential sampling approach for global metamodeling. J. Mech. Des., 138.","DOI":"10.1115\/1.4031905"},{"key":"ref_32","unstructured":"Jin, R., Chen, W., and Sudjianto, A. (October, January 29). On sequential sampling for global metamodeling in engineering design. Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"670","DOI":"10.2514\/1.J052375","article-title":"Special section on multidisciplinary design optimization: Metamodeling in multidisciplinary design optimization: How far have we really come?","volume":"52","author":"Viana","year":"2014","journal-title":"AIAA J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.strusafe.2011.01.002","article-title":"AK-MCS: An active learning reliability method combining Kriging and Monte Carlo simulation","volume":"33","author":"Echard","year":"2011","journal-title":"Struct. Saf."},{"key":"ref_35","unstructured":"Settles, B. (2009). Active Learning Literature Survey (Computer Sciences Technical Report 1648), University of Wisconsin-Madison."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2379776.2379786","article-title":"Ensemble approaches for regression: A survey","volume":"45","author":"Soares","year":"2012","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.ifacol.2015.12.183","article-title":"A novel sequential exploration-exploitation sampling strategy for global metamodeling","volume":"48","author":"Jiang","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1108\/02644401311329352","article-title":"Adaptive sampling strategies for non-intrusive POD-based surrogates","volume":"30","author":"Vasile","year":"2013","journal-title":"Eng. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, S., Liu, H., Wang, X., and Jiang, X. (2014). A robust error-pursuing sequential sampling approach for global metamodeling based on voronoi diagram and cross validation. J. Mech. Des., 136.","DOI":"10.1115\/1.4027161"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"A1020","DOI":"10.1137\/140962437","article-title":"A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments","volume":"37","author":"Couckuyt","year":"2015","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pan, G., Ye, P., Wang, P., and Yang, Z. (2014). A sequential optimization sampling method for metamodels with radial basis functions. Sci. World J., 2014.","DOI":"10.1155\/2014\/192862"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s11081-010-9118-y","article-title":"Sequential approximate optimization using radial basis function network for engineering optimization","volume":"12","author":"Kitayama","year":"2011","journal-title":"Optim. Eng."},{"key":"ref_43","unstructured":"Van Rossum, G., and Drake, F.L. (2009). Python 3 Reference Manual, CreateSpace."},{"key":"ref_44","unstructured":"(MATLAB, 2010). MATLAB, The Math Works, Version 9.8 (R2020a)."},{"key":"ref_45","unstructured":"(Multiphysics, C., 2018). Multiphysics, C., Version 5.4."},{"key":"ref_46","unstructured":"(SolidWorks, 2020). SolidWorks, Version 2020 SP2.0."},{"key":"ref_47","unstructured":"Duchanoy, C.A., Calvo, H., and Moreno-Armend\u00e1riz, M.A. (2020, September 07). ASAMS. Available online: https:\/\/github.com\/Duchanoy\/ASAMS."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neunet.2015.10.007","article-title":"TWSVR: Regression via twin support vector machine","volume":"74","author":"Khemchandani","year":"2016","journal-title":"Neural Netw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TASLP.2014.2367814","article-title":"Random regression forests for acoustic event detection and classification","volume":"23","author":"Phan","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nguyen, H.M., Kalra, G., Jun, T.J., and Kim, D. (2018). A Novel Echo State Network Model Using Bayesian Ridge Regression and Independent Component Analysis. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-01421-6_3"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ress.2018.11.002","article-title":"A general failure-pursuing sampling framework for surrogate-based reliability analysis","volume":"183","author":"Jiang","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/nme.2750","article-title":"An algorithm for fast optimal Latin hypercube design of experiments","volume":"82","author":"Viana","year":"2010","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0169-7161(03)22006-X","article-title":"Uniform experimental designs and their applications in industry","volume":"22","author":"Fang","year":"2003","journal-title":"Handb. Stat."},{"key":"ref_54","first-page":"185063","article-title":"A review of constraint-handling techniques for evolution strategies","volume":"2010","author":"Kramer","year":"2010","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4295","DOI":"10.1016\/j.jcp.2012.02.014","article-title":"An improved bounce-back scheme for complex boundary conditions in lattice Boltzmann method","volume":"231","author":"Yin","year":"2012","journal-title":"J. Comput. Phys."},{"key":"ref_56","first-page":"154","article-title":"Constrained construction of planar delaunay triangulations without flipping","volume":"14","author":"Galishnikova","year":"2018","journal-title":"Structural-Mechanics"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/0167-4730(90)90012-E","article-title":"A fast and efficient response surface approach for structural reliability problems","volume":"7","author":"Bucher","year":"1990","journal-title":"Struct. Saf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s00158-015-1347-4","article-title":"Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis","volume":"53","author":"Hu","year":"2016","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.ress.2017.09.008","article-title":"A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis","volume":"169","author":"Xiao","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"01013","DOI":"10.1051\/matecconf\/201929001013","article-title":"Optimizing the magnetic circuit of an actuator","volume":"Volume 290","author":"Popescu","year":"2019","journal-title":"MATEC Web of Conferences"},{"key":"ref_61","unstructured":"Ribas, S., Ribeiro-Neto, B., and Ziviani, N. (2013). R-Score: Reputation-based Scoring of Research Groups. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:00Z","timestamp":1760177460000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,17]]},"references-count":61,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185332"],"URL":"https:\/\/doi.org\/10.3390\/s20185332","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,9,17]]}}}