{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:01:34Z","timestamp":1780761694916,"version":"3.54.1"},"reference-count":98,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T00:00:00Z","timestamp":1777766400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Science"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.jocs.2026.102886","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:21:01Z","timestamp":1777735261000},"page":"102886","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Robust ensemble learning framework through symbolic regression for soft sensor modeling"],"prefix":"10.1016","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1202-5355","authenticated-orcid":false,"given":"Iron","family":"Tessaro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-3902","authenticated-orcid":false,"given":"Helon Vicente","family":"Hultmann Ayala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2490-4568","authenticated-orcid":false,"given":"Viviana","family":"Cocco Mariani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5728-943X","authenticated-orcid":false,"given":"Leandro","family":"dos Santos Coelho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.jocs.2026.102886_b1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.compchemeng.2008.12.012","article-title":"Data-driven soft sensors in the process industry","volume":"33","author":"Kadlec","year":"2009","journal-title":"Comput. Chem. Eng."},{"issue":"9","key":"10.1016\/j.jocs.2026.102886_b2","doi-asserted-by":"crossref","first-page":"5853","DOI":"10.1109\/TII.2021.3053128","article-title":"A survey on deep learning for data-driven soft sensors","volume":"17","author":"Sun","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.chemolab.2015.12.011","article-title":"Review of soft sensor methods for regression applications","volume":"152","author":"Souza","year":"2016","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.jocs.2026.102886_b4","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jprocont.2020.03.012","article-title":"Rebooting data-driven soft-sensors in process industries: A review of kernel methods","volume":"89","author":"Liu","year":"2020","journal-title":"J. Process Control"},{"key":"10.1016\/j.jocs.2026.102886_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101434","article-title":"Uncertainty-aware soft sensor using Bayesian recurrent neural networks","volume":"50","author":"Lee","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102255","article-title":"A modeling method of wide random forest multi-output soft sensor with attention mechanism for quality prediction of complex industrial processes","volume":"59","author":"Wan","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101590","article-title":"Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling","volume":"52","author":"Wang","year":"2022","journal-title":"Adv. Eng. Inform."},{"issue":"10","key":"10.1016\/j.jocs.2026.102886_b8","doi-asserted-by":"crossref","DOI":"10.3390\/s22103838","article-title":"A tinyml soft-sensor approach for low-cost detection and monitoring of vehicular emissions","volume":"22","author":"Andrade","year":"2022","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.jocs.2026.102886_b9","article-title":"Soft sensors for state of charge, state of energy and power loss in formula student electric vehicle","volume":"4","author":"Purohit","year":"2021","journal-title":"Appl. Syst. Innov."},{"key":"10.1016\/j.jocs.2026.102886_b10","first-page":"1","article-title":"Soft sensor application to predict remaining useful life of components of the exhaust system of diesel engines","volume":"vol. 2020-January","author":"Diesch","year":"2020"},{"issue":"7","key":"10.1016\/j.jocs.2026.102886_b11","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TII.2018.2809730","article-title":"Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE","volume":"14","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b12","doi-asserted-by":"crossref","first-page":"88949","DOI":"10.1109\/ACCESS.2019.2925048","article-title":"Development cost optimization for multi-functional mixed-criticality embedded systems","volume":"7","author":"Wei","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s21030823","article-title":"RNN- and LSTM-based soft sensors transferability for an industrial process","volume":"21","author":"Curreri","year":"2021","journal-title":"Sensors (Switzerland)"},{"issue":"8","key":"10.1016\/j.jocs.2026.102886_b14","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.compchemeng.2003.11.004","article-title":"Soft sensing modeling based on support vector machine and Bayesian model selection","volume":"28","author":"Yan","year":"2004","journal-title":"Comput. Chem. Eng."},{"issue":"2 February","key":"10.1016\/j.jocs.2026.102886_b15","article-title":"Review of machine learning methods in soft robotics","volume":"16","author":"Kim","year":"2021","journal-title":"PLoS One"},{"key":"10.1016\/j.jocs.2026.102886_b16","doi-asserted-by":"crossref","first-page":"154096","DOI":"10.1109\/ACCESS.2019.2949286","article-title":"Black-box vs. White-box: Understanding their advantages and weaknesses from a practical point of view","volume":"7","author":"Loyola-Gonzalez","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.jocs.2026.102886_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2019.106696","article-title":"Deep hybrid modeling of chemical process: Application to hydraulic fracturing","volume":"134","author":"Bangi","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.jocs.2026.102886_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.jaerosci.2020.105694","article-title":"Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration","volume":"152","author":"Fung","year":"2021","journal-title":"J. Aerosol Sci."},{"issue":"7","key":"10.1016\/j.jocs.2026.102886_b19","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1007\/s10462-020-09816-7","article-title":"A comprehensive survey on model compression and acceleration","volume":"53","author":"Choudhary","year":"2020","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b20","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/5.558716","article-title":"Embedded software in real-time signal processing systems: Application and architecture trends","volume":"85","author":"Paulin","year":"1997","journal-title":"Proc. IEEE"},{"issue":"5","key":"10.1016\/j.jocs.2026.102886_b21","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TCST.2008.2004503","article-title":"A system-on-a-chip implementation for embedded real-time model predictive control","volume":"17","author":"Vouzis","year":"2009","journal-title":"IEEE Trans. Control Syst. Technol."},{"issue":"6","key":"10.1016\/j.jocs.2026.102886_b22","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1109\/TII.2017.2768075","article-title":"Hardware cost design optimization for functional safety-critical parallel applications on heterogeneous distributed embedded systems","volume":"14","author":"Xie","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b23","series-title":"Proceedings - MSE 2007: 2007 IEEE International Conference on Microelectronic Systems Education: Educating Systems Designers for the Global Economy and a Secure World","first-page":"162","article-title":"Using a low-cost SoC computer and a commercial RTOS in an embedded systems design course","author":"Hamblen","year":"2007"},{"key":"10.1016\/j.jocs.2026.102886_b24","series-title":"4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings","first-page":"269","article-title":"Tinyml: A systematic review and synthesis of existing research","author":"Han","year":"2022"},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MCAS.2020.3005467","article-title":"Tinyml-enabled frugal smart objects: Challenges and opportunities","volume":"20","author":"Sanchez-Iborra","year":"2020","journal-title":"IEEE Circuits Syst. Mag."},{"issue":"12","key":"10.1016\/j.jocs.2026.102886_b26","doi-asserted-by":"crossref","DOI":"10.3390\/fi14120363","article-title":"Tinyml for ultra-low power AI and large scale IoT deployments: A systematic review","volume":"14","author":"Schizas","year":"2022","journal-title":"Futur. Internet"},{"issue":"5","key":"10.1016\/j.jocs.2026.102886_b27","doi-asserted-by":"crossref","first-page":"6859","DOI":"10.1109\/TII.2022.3181692","article-title":"A self-interpretable soft sensor based on deep learning and multiple attention mechanism: From data selection to sensor modeling","volume":"19","author":"Guo","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jocs.2026.102886_b28","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/B978-0-443-15274-0.50243-2","article-title":"Symbolic regression based interpretable data-driven soft-sensor for process quality control","volume":"52","author":"Kay","year":"2023","journal-title":"Comput. Aided Chem. Eng."},{"issue":"9","key":"10.1016\/j.jocs.2026.102886_b29","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1021\/acs.iecr.3c04021","article-title":"Constructing a symbolic regression-based interpretable soft sensor for industrial data analytics and product quality control","volume":"63","author":"Kay","year":"2024","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.jocs.2026.102886_b30","series-title":"A soft sensor method with uncertainty-awareness and self-explanation based on large language models enhanced by domain knowledge retrieval","author":"Tong","year":"2025"},{"key":"10.1016\/j.jocs.2026.102886_b31","article-title":"Comprehensive analysis on machine learning approaches for interpretable and stable soft sensors","author":"Cao","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"8","key":"10.1016\/j.jocs.2026.102886_b32","doi-asserted-by":"crossref","first-page":"4945","DOI":"10.1021\/acs.jctc.2c00281","article-title":"Improving symbolic regression for predicting materials properties with iterative variable selection","volume":"18","author":"Guo","year":"2022","journal-title":"J. Chem. Theory Comput."},{"issue":"2","key":"10.1016\/j.jocs.2026.102886_b33","doi-asserted-by":"crossref","DOI":"10.3847\/1538-4357\/ad014c","article-title":"Deep symbolic regression for physics guided by units constraints: Toward the automated discovery of physical laws","volume":"959","author":"Tenachi","year":"2023","journal-title":"Astrophys. J."},{"key":"10.1016\/j.jocs.2026.102886_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2023.116647","article-title":"Discovering a reaction\u2013diffusion model for alzheimer\u2019s disease by combining PINNs with symbolic regression","volume":"419","author":"Zhang","year":"2024","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"issue":"4","key":"10.1016\/j.jocs.2026.102886_b35","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1177\/004912417800600405","article-title":"Interpreting polynomial regression","volume":"6","author":"Stimson","year":"1978","journal-title":"Sociol. Methods Res."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b36","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.94.012214","article-title":"Prediction of dynamical systems by symbolic regression","volume":"94","author":"Quade","year":"2016","journal-title":"Phys. Rev. E"},{"key":"10.1016\/j.jocs.2026.102886_b37","series-title":"2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings","first-page":"1","article-title":"Space and time efficiency analysis of data-driven methods applied to embedded systems","author":"Tessaro","year":"2021"},{"key":"10.1016\/j.jocs.2026.102886_b38","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.apm.2023.08.009","article-title":"Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming","volume":"125","author":"Bardhan","year":"2024","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.jocs.2026.102886_b39","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.apm.2018.01.015","article-title":"Evolving chaos: Identifying new attractors of the generalised lorenz family","volume":"57","author":"Pan","year":"2018","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.jocs.2026.102886_b40","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/B978-0-443-15274-0.50243-2","article-title":"Symbolic regression based interpretable data-driven soft-sensor for process quality control","volume":"52","author":"Kay","year":"2023","journal-title":"Comput. Aided Chem. Eng."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b41","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10107-018-1289-x","article-title":"A global MINLP approach to symbolic regression","volume":"170","author":"Cozad","year":"2018","journal-title":"Math. Program."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10710-020-09387-0","article-title":"Benchmarking state-of-the-art symbolic regression algorithms","volume":"22","author":"\u017degklitz","year":"2021","journal-title":"Genet. Program. Evolvable Mach."},{"key":"10.1016\/j.jocs.2026.102886_b43","first-page":"i","article-title":"New methods and applications in multiple attribute decision making (madm)","volume":"277","author":"Alinezhad","year":"2019","journal-title":"Int. Ser. Oper. Res. Manag. Sci."},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b44","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1057\/jors.1987.44","article-title":"A reconciliation among discrete compromise solutions","volume":"38","author":"Yoon","year":"1987","journal-title":"J. Oper. Res. Soc."},{"key":"10.1016\/j.jocs.2026.102886_b45","first-page":"1","article-title":"Contemporary symbolic regression methods and their relative performance","volume":"vol. 1","author":"La Cava","year":"2021"},{"key":"10.1016\/j.jocs.2026.102886_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2023.109269","article-title":"Ensemble learning methods using the hodrick\u2013prescott filter for fault forecasting in insulators of the electrical power grids","volume":"152","author":"Seman","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.jocs.2026.102886_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruc.2021.106557","article-title":"Development of interpretable, data-driven plasticity models with symbolic regression","volume":"252","author":"Bomarito","year":"2021","journal-title":"Comput. Struct."},{"key":"10.1016\/j.jocs.2026.102886_b48","article-title":"Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process","volume":"6","author":"Asadzadeh","year":"2021","journal-title":"Appl. Eng. Sci."},{"issue":"4","key":"10.1016\/j.jocs.2026.102886_b49","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1023\/A:1009868929893","article-title":"The role of Occam\u2019s Razor in knowledge discovery","volume":"3","author":"Domingos","year":"1999","journal-title":"Data Min. Knowl. Discov."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b50","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-023-10622-0","article-title":"Interpretable scientific discovery with symbolic regression: a review","volume":"57","author":"Makke","year":"2024","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"10.1016\/j.jocs.2026.102886_b51","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/BF00175355","article-title":"Genetic programming as a means for programming computers by natural selection","volume":"4","author":"Koza","year":"1994","journal-title":"Stat. Comput."},{"key":"10.1016\/j.jocs.2026.102886_b52","unstructured":"B.K. Petersen, M.L. Larma, T.N. Mundhenk, C.P. Santiago, S.K. Kim, J.T. Kim, Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients, in: ICLR 2021 - 9th International Conference on Learning Representations, 2021, pp. 1\u201326."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b53","doi-asserted-by":"crossref","DOI":"10.1007\/s42452-019-1734-3","article-title":"Symbolic regression by uniform random global search","volume":"2","author":"Towfighi","year":"2020","journal-title":"SN Appl. Sci."},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b54","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s10710-019-09371-3","article-title":"Parameter identification for symbolic regression using nonlinear least squares","volume":"21","author":"Kommenda","year":"2020","journal-title":"Genet. Program. Evolvable Mach."},{"issue":"2","key":"10.1016\/j.jocs.2026.102886_b55","doi-asserted-by":"crossref","DOI":"10.1145\/3597312","article-title":"Transformation-interaction-rational representation for symbolic regression: A detailed analysis of srbench results","volume":"3","author":"De Fran\u00e7a","year":"2023","journal-title":"ACM Trans. Evol. Learn. Optim."},{"key":"10.1016\/j.jocs.2026.102886_b56","first-page":"1","article-title":"End-to-end symbolic regression with transformers","volume":"vol. 35","author":"Kamienny","year":"2022"},{"key":"10.1016\/j.jocs.2026.102886_b57","series-title":"GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference","first-page":"2282","article-title":"Bingo: A customizable framework for symbolic regression with genetic programming","author":"Randall","year":"2022"},{"key":"10.1016\/j.jocs.2026.102886_b58","series-title":"Interpretable machine learning for science with PySR and SymbolicRegression.jl","author":"Cranmer","year":"2023"},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b59","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-023-00743-2","article-title":"RILS-rols: robust symbolic regression via iterated local search and ordinary least squares","volume":"10","author":"Kartelj","year":"2023","journal-title":"J. Big Data"},{"issue":"9","key":"10.1016\/j.jocs.2026.102886_b60","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun. ACM"},{"key":"10.1016\/j.jocs.2026.102886_b61","unstructured":"H. Drucker, C.J. Surges, L. Kaufman, A. Smola, V. Vapnik, Support vector regression machines, in: Advances in Neural Information Processing Systems, 1997, pp. 155\u2013161."},{"issue":"8","key":"10.1016\/j.jocs.2026.102886_b62","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b63","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.jocs.2026.102886_b64","first-page":"6638","article-title":"Catboost: Unbiased boosting with categorical features","volume":"vol. 2018-December","author":"Prokhorenkova","year":"2018"},{"key":"10.1016\/j.jocs.2026.102886_b65","first-page":"39","article-title":"Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees","volume":"11832 LNCS","author":"Guryanov","year":"2019","journal-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)"},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b66","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B: Methodol."},{"key":"10.1016\/j.jocs.2026.102886_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijheatmasstransfer.2020.120097","article-title":"Integrative numerical modeling and thermodynamic optimal design of counter-flow plate-fin heat exchanger applying neural networks","volume":"159","author":"Richter do Nascimento","year":"2020","journal-title":"Int. J. Heat Mass Transfer"},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b68","doi-asserted-by":"crossref","DOI":"10.3390\/en16031371","article-title":"Aggregating prophet and seasonal trend decomposition for time series forecasting of Italian electricity spot prices","volume":"16","author":"Stefenon","year":"2023","journal-title":"Energies"},{"key":"10.1016\/j.jocs.2026.102886_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2022.108504","article-title":"Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach","volume":"143","author":"da Silva","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.jocs.2026.102886_b70","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.egypro.2015.11.889","article-title":"Automotive turbochargers power estimation based on speed fluctuation analysis","volume":"82","author":"Ravaglioli","year":"2015","journal-title":"Energy Procedia"},{"key":"10.1016\/j.jocs.2026.102886_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109828","article-title":"Design and experimental investigation of a herringbone grooved gas bearing supported turbocharger","volume":"186","author":"Liu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.jocs.2026.102886_b72","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.ymssp.2014.11.006","article-title":"Study of the turbocharger shaft motion by means of infrared sensors","volume":"56","author":"Serrano","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.jocs.2026.102886_b73","doi-asserted-by":"crossref","DOI":"10.4271\/2009-01-1022","article-title":"Upgrade of a turbocharger speed measurement algorithm based on acoustic emission","author":"Moro","year":"2009","journal-title":"SAE Tech. Pap."},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b74","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.4271\/2014-01-1645","article-title":"Non-intrusive methodology for estimation of speed fluctuations in automotive turbochargers under unsteady flow conditions","volume":"7","author":"Ponti","year":"2014","journal-title":"SAE Int. J. Engines"},{"key":"10.1016\/j.jocs.2026.102886_b75","first-page":"11040","article-title":"Implications of using turbocharger speed sensor for boost pressure control","volume":"vol. 50","author":"Holmbom","year":"2017"},{"issue":"3","key":"10.1016\/j.jocs.2026.102886_b76","doi-asserted-by":"crossref","DOI":"10.1115\/1.4049825","article-title":"Evaluation of the rotor temperature distribution of an automotive turbocharger under hot gas conditions including indirect experimental validation","volume":"143","author":"Zeh","year":"2021","journal-title":"J. Eng. Gas Turbines Power"},{"key":"10.1016\/j.jocs.2026.102886_b77","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2020.105505","article-title":"Efficient computational modelling of low loaded bearings of turbocharger rotors","volume":"174","author":"Novotn\u00fd","year":"2020","journal-title":"Int. J. Mech. Sci."},{"issue":"20","key":"10.1016\/j.jocs.2026.102886_b78","doi-asserted-by":"crossref","first-page":"4851","DOI":"10.1016\/j.jsv.2011.04.031","article-title":"Dynamic behaviours of a full floating ring bearing supported turbocharger rotor with engine excitation","volume":"330","author":"Tian","year":"2011","journal-title":"J. Sound Vib."},{"key":"10.1016\/j.jocs.2026.102886_b79","series-title":"Development of a Worldwide Harmonised Heavy-duty Engine Emissions Test Cycle Final Report","author":"Steven","year":"2001"},{"key":"10.1016\/j.jocs.2026.102886_b80","article-title":"Sympy: Symbolic computing in python","volume":"3","author":"Meurer","year":"2017","journal-title":"PeerJ"},{"issue":"7","key":"10.1016\/j.jocs.2026.102886_b81","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/T-C.1975.224297","article-title":"A branch and bound algorithm for computing k-nearest neighbors","volume":"C-24","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.jocs.2026.102886_b82","first-page":"3147","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"vol. 2017-December","author":"Ke","year":"2017"},{"issue":"2","key":"10.1016\/j.jocs.2026.102886_b83","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of prediction error by using K-fold cross-validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat. Comput."},{"key":"10.1016\/j.jocs.2026.102886_b84","series-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2623","article-title":"Optuna: A next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"issue":"D7","key":"10.1016\/j.jocs.2026.102886_b85","doi-asserted-by":"crossref","first-page":"7183","DOI":"10.1029\/2000JD900719","article-title":"Summarizing multiple aspects of model performance in a single diagram","volume":"106","author":"Taylor","year":"2001","journal-title":"J. Geophys. Res. Atmospheres"},{"key":"10.1016\/j.jocs.2026.102886_b86","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.136027","article-title":"Peak in-cylinder pressure virtual sensor based on hybrid modeling framework","volume":"326","author":"Tessaro","year":"2025","journal-title":"Energy","ISSN":"https:\/\/id.crossref.org\/issn\/0360-5442","issn-type":"print"},{"issue":"4","key":"10.1016\/j.jocs.2026.102886_b87","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1109\/TRO.2020.2988642","article-title":"TossingBot: Learning to throw arbitrary objects with residual physics","volume":"36","author":"Zeng","year":"2020","journal-title":"IEEE Trans. Robot."},{"issue":"5","key":"10.1016\/j.jocs.2026.102886_b88","doi-asserted-by":"crossref","first-page":"5136","DOI":"10.1109\/TIE.2024.3476977","article-title":"A PINN-based friction-inclusive dynamics modeling method for industrial robots","volume":"72","author":"Hu","year":"2025","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.jocs.2026.102886_b89","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110405","article-title":"Hybrid gray and black-box nonlinear system identification of an elastomer joint flexible robotic manipulator","volume":"200","author":"de Sousa","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.jocs.2026.102886_b90","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109815","article-title":"Nonlinear ensemble gray and black-box system identification of friction induced vibrations in slender rotating structures","volume":"186","author":"Pires","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.jocs.2026.102886_b91","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.128066","article-title":"Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering","volume":"280","author":"Cesar de Lima Nogueira","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.jocs.2026.102886_b92","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2023.129366","article-title":"CO and NOx emissions prediction in gas turbine using a novel modeling pipeline based on the combination of deep forest regressor and feature engineering","volume":"355","author":"dos Santos Coelho","year":"2024","journal-title":"Fuel"},{"key":"10.1016\/j.jocs.2026.102886_b93","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.jocs.2026.102886_b94","doi-asserted-by":"crossref","DOI":"10.1214\/21-SS133","article-title":"Interpretable machine learning: Fundamental principles and 10 grand challenges","volume":"16","author":"Rudin","year":"2022","journal-title":"Stat. Surv."},{"key":"10.1016\/j.jocs.2026.102886_b95","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101805","article-title":"Explainable artificial intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence","volume":"99","author":"Ali","year":"2023","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.jocs.2026.102886_b96","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103900","article-title":"A comprehensive study on fidelity metrics for XAI","volume":"62","author":"Mir\u00f3-Nicolau","year":"2025","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.jocs.2026.102886_b97","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2022.115732","article-title":"Automated learning of interpretable models with quantified uncertainty","volume":"403","author":"Bomarito","year":"2023","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.jocs.2026.102886_b98","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101882","article-title":"Exploring the balance between interpretability and performance with carefully designed constrainable neural additive models","volume":"99","author":"Mariotti","year":"2023","journal-title":"Inf. Fusion"}],"container-title":["Journal of Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877750326001043?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877750326001043?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:41:05Z","timestamp":1780760465000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877750326001043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":98,"alternative-id":["S1877750326001043"],"URL":"https:\/\/doi.org\/10.1016\/j.jocs.2026.102886","relation":{},"ISSN":["1877-7503"],"issn-type":[{"value":"1877-7503","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Robust ensemble learning framework through symbolic regression for soft sensor modeling","name":"articletitle","label":"Article Title"},{"value":"Journal of Computational Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jocs.2026.102886","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102886"}}