Uncertainty Quantification for Regression in Engineering Design: Calibration Under Extrapolation

Authors

  • Isotta Pignatelli Department of Statistics, Polesine Institute of Data Science, Rovigo, Italy Author
  • Aristeidis Roussopoulou Centre for Materials Informatics, Laconia Centre for Machine Intelligence, Sparti, Greece Author
  • Krasimir Peevski Department of Operations Research, Dobrudzha Institute of Applied Artificial Intelligence, Dobrich, Bulgaria Author

DOI:

https://doi.org/10.5281/zenodo.22660957

Keywords:

Uncertainty Quantification for Regression in Engineering Design; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Uncertainty Quantification for Regression in Engineering Design is an area of active development in applied machine learning,
with evidence that has accumulated unevenly across its component questions. We review the current evidence base for uncertainty
quantification for regression in engineering design, spanning methodological, implementation, governance, and cost dimensions,
focusing on where practice has converged and where it continues to vary, and set out the practical considerations relevant to a
team considering its adoption. We conclude that selective, context-specific use is best supported by the evidence currently
available, rather than uniform adoption or avoidance

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Alvez D, Dumitrescu A, et al. "Uncertainty Quantification for Regression in Engineering Design: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.2196/48633

Brankovic K, Kowalczyk B, et al. "Uncertainty Quantification for Regression in Engineering Design: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.1371/journal.pone.0305949

Castellani R, Ristic C, et al. "Uncertainty Quantification for Regression in Engineering Design: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1371/journal.pdig.0000514

Dumitrescu A, Andric D, et al. "Uncertainty Quantification for Regression in Engineering Design: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.17219/acem/208596

Eriksdottir B, Brankovic E, et al. "Uncertainty Quantification for Regression in Engineering Design: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.7759/cureus.104086

Farkas I, Ionescu F, et al. "Uncertainty Quantification for Regression in Engineering Design: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1038/s41746-023-00852-5

Grabowski P, Petrescu G, et al. "Uncertainty Quantification for Regression in Engineering Design: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.1016/j.mayocpiqo.2022.09.004

Hendriksen W, Wojcik H, et al. "Uncertainty Quantification for Regression in Engineering Design: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.1177/08404704241291226

Ionescu F, Foscarini I, et al. "Uncertainty Quantification for Regression in Engineering Design: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.1093/medlaw/fwad013

Jankowiak G, Grabowski J, et al. "Uncertainty Quantification for Regression in Engineering Design: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.2196/89834

Kowalczyk N, Novotna K, et al. "Uncertainty Quantification for Regression in Engineering Design: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.1007/s10916-024-02104-9

Lindqvist U, Urbanek L, et al. "Uncertainty Quantification for Regression in Engineering Design: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.3389/fdgth.2026.1896184

Marchetti D, Doncheva M, et al. "Uncertainty Quantification for Regression in Engineering Design: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.3390/diagnostics15162117

Novotna E, Eriksdottir N, et al. "Uncertainty Quantification for Regression in Engineering Design: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.4103/jrpp.jrpp_20_65

Ostrowski L, Lindqvist O, et al. "Uncertainty Quantification for Regression in Engineering Design: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.3389/fdgth.2026.1763884

Petrescu S, Szabo P, et al. "Uncertainty Quantification for Regression in Engineering Design: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.2196/48297

Quintero B, Balogh Q, et al. "Uncertainty Quantification for Regression in Engineering Design: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.1007/s00330-025-12031-z

Ristic C, Castellani R, et al. "Uncertainty Quantification for Regression in Engineering Design: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.3389/fdgth.2026.1699005

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Wojcik H, Hendriksen W, et al. "Uncertainty Quantification for Regression in Engineering Design: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.2196/48633

Zielinski O, Ostrowski Z, et al. "Uncertainty Quantification for Regression in Engineering Design: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.1371/journal.pone.0305949

Andric V, Vasquez A, et al. "Uncertainty Quantification for Regression in Engineering Design: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.1371/journal.pdig.0000514

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Published

2026-01-03

Issue

Vol. 5 No. 1 (2026): vol 5 issue 1 2026

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Articles

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