Cited by 311Advanced Multi-Objective Optimization AlgorithmsProbabilistic and Robust Engineering DesignComputational Fluid Dynamics and Aerodynamics

The DOI matches its Crossref record; 539 of the 548 references it cites were found in a registry or an index.

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

Yondo, Raul, Andrés, Esther, Valero, Eusebio·2018·Progress in Aerospace Sciences

DOI 10.1016/j.paerosci.2017.11.003

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.paerosci.2017.11.003

Cites

539 of 539 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,178
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067

Show all 548 references

Research works printed but not linked: 40

Koziel, 2011, 45(1–3), 33
Viana, 2014, Metamodeling in multidisciplinary design optimization: how far have we really come?, J. Prog. Aerosp. Sci., 52, 670
Koehler, 1996, Computer experiments, J. Prog. Aerosp. Sci., 261
Finney, 1945, vol. 12, 291
Fries, 1980, Minimum aberration 2k–p designs, Technometrics, 22, 601
Rechtschaffner, 2005, Saturated fractions of 2n and 3n factorial designs, Technometrics, 9, 569
Box, 1961, The 2k–p fractional factorial designs, Part I, Technometrics, 3, 311
Box, 1961, The 2k–p fractional factorial designs, Part II, Technometrics, 3, 449
Yue, 1999, Robust optimal designs for fitting approximately linear models, Stat. Sin., 9, 1053
Owen, 1992, Randomly orthogonal arrays for computer experiments, integration and visualization, Stat. Sin., 2, 439
Fang, 1980, The uniform design: application of number theoretic methods in experimental design, Acta Math. Appl. Sin., 3, 362
S. Mak and V. R. Joseph, Minimax designs using clustering. J. Comput. Graph. Stat., submitted for publication, arxiv.org/abs/1602.03938. · arXiv
B.G.M. Husslage, reportMaximin Designs for Computer Experiments. (Doctoral dissertation), Tilburg University, Tilburg, Netherlands.
Husslage, 2006, vol. 18
Yondo, 2016, A 2D numerical study of the influence of a priori sampling methods on surrogate models accuracy in aircraft aerodynamic design optimization, vol. 510
Klein, 1983, vol. 2126
Jouke H.S. de Baar, Thomas P. Scholcz, Clemens V. Verhoosel, Richard P. Dwight, Alexander H. van Zuijlen and Hester Bijl, Efficient uncertainty quantification with gradient-enhanced Kriging: applications in FSI, In J. Eberhardsteiner et al (eds.). European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), 51, Vienna, Austria, September 10-14, 2012.
Le Gratiet, 2013, vol. 2
Broomhead, 1988, Multivariate functional interpolation and adaptative networks, J. Complex Syst., 2, 321
Quarteroni, 2014, vol. 9
Kroll, 2012, CEAS Aeronaut. J., 151
Loéve, 1945, Functions Aleatoire de Second Ordre, C. R. Acad. Sci. Paris, 220
Obukbov, 1954, Statistical description of continuous fields, Tr. Geophys. Int. Akad. Nauk. SSSR, 24, 3
Pougachev, 1953, General theory of the correlations of random functions, Izv. Akud. Nauk. SSSR, Set. Mat., 17
Chen, 2012, Support-vector-machine-based reduced-order model for limit cycle oscillation prediction of nonlinear aeroelastic system, Math. Prob. Eng., 10.1155/2012/152123 · DOI
Iuliano, 2013, Proper orthogonal decomposition, surrogate Modelling and evolutionary optimization in aerodynamic design, J. Comput. Fluids, 84, 327, 10.1016/j.compfluid.2013.06.007 · DOI
Chinesta, 2011, Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models, J. Fluids Eng., 18, 395
Amsallem, 2008, Interpolation method for adapting reduced-order models and application to aeroelasticity, AIAA J., 46, 1803, 10.2514/1.35374 · DOI
Amsallem, 2012, Nonlinear model order reduction based on local reduced-order bases, Int. J. Numer. Methods Eng., 92, 891, 10.1002/nme.4371 · DOI
Wirtz, 2014, A posteriori error estimation for DEIM reduced nonlinear dynamical systems, SIAM J. Sci. Comput., 36, A311, 10.1137/120899042 · DOI
Zhong-Hua Han, Stefan Görtz and Ralf Zimmermann, On improving efficiency and accuracy of variable-fidelity surrogate modeling in aero-data for loads context. CEAS 2009 European Air and Space Conference, Manchester U. K., 26–29 Oct. 2009.
Craig, 1998, Constructing partial prior specifications for models of complex physical systems, Appl. Stat., 47, 37
Zimmermann, 2012, Gradient-enhanced surrogate modeling based on proper orthogonal decomposition, Struct. Multidiscip. Optim., 237, 403
Laurenceau, 2010, Comparison of gradient-based and gradient-enhanced response-surface-based optimizers, AIAA J., 48, 981, 10.2514/1.45331 · DOI
Han, 2010, Towards efficient high-dimensional aerodynamic shape optimization: surrogate modeling via gradient-enhanced Kriging, vol. 1
Voutchkov, 2010, vol. 7, 155
James Parr, C. M. E. Holden, A. I. J. Forrester and A. J. Keane, Review of efficient surrogate infill sampling criteria with constraint handling. 2nd International Conference on Engineering Optimization, 1–10, Portugal, 06-10 Sep 2011.
Huang, 2013, Research on multi-fidelity aerodynamic optimization methods, Chin. J. Aeronaut., 26, 279, 10.1016/j.cja.2013.02.004 · DOI
Zahr, 2015, Progressive construction of a parametric reduced-order model for PDE-constrained optimization, Int. J. Numer. Methods Eng., 102, 1111, 10.1002/nme.4770 · DOI
Allaire, 2010, Surrogate modeling for uncertainty assessment with application to aviation environmental system models, AIAA J., 48, 1791, 10.2514/1.J050247 · DOI

Sorted by citations

Items 201–300 of 310

A Design of Experiments Approach Towards Desired Flow Distribution Through Manifolds in Electronics Cooling
Sogunuru, A., Varughese, S., Niranjanappa, A., & Hemachandra Reddy, K. (2022) · Defence Science Journal · doi:10.14429/dsj.72.17883 · Cited by 3
Expensive Inequality Constraints Handling Methods Suitable for Dynamic Surrogate-based Optimization
Li, C., Fang, H., & Gong, C. (2019) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790253 · Cited by 3
“Researchers have delivered a great deal of fruitful results [5,6] in many aspects, such as surrogate modeling, design of experiment (DoE), refinement strategies, and so on.”
Background
Surrogate Model-Based Rapid optimization for the Deployment of Combat System of Systems
Ge, B., Xia, B., Yang, K., Zhao, Q., Dou, Y., & Xiang, Z. (2020) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon47679.2020.9275874 · Cited by 3
“When facing complicated and black-box optimization problem, these traditional methods tend to be inapplicable or inefficient [4].”
Method
Procedures for characterization of compressive material parameters for GRE pipes, using flexural modulus test results and inverse analysis
Hosnifam, S., Mahini, M. R., & Cocchetti, G. (2023) · Meccanica · doi:10.1007/s11012-023-01659-0 · Cited by 2
“[50, 51]), but the choice of the optimal surrogate model requires a specific campaign (see e.”
Background
Prediction of CFD Simulation Results Using Machine-Learning Models and Process Designs Based on Direct Inverse Analysis of the Models
Kaneko, H. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c03669 · Cited by 2
On deep-learning-based closures for algebraic surrogate models of turbulent flows
Eiximeno, B., Sanchís-Agudo, M., Miró, A., Rodriguez, I., Vinuesa, R., & Lehmkuhl, O. (2025) · Journal of Fluid Mechanics · doi:10.1017/jfm.2025.10610 · Cited by 2
Experimental study on the underwater propulsion performance of bionic flexible flapping fins
Zhou, M., Wu, Z., & Yang, C. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.123146 · Cited by 2
Manifold-Preserving Guided Aerodynamic Modeling for Heterogeneous Data
Liu, Y., Liu, Z.-T., Guo, S.-Q., Nie, H., Liu, W., Zhang, C.-A., & Yan, X. (2025) · AIAA Journal · doi:10.2514/1.j065256 · Cited by 2
Surrogate-assisted evolutionary optimization and microgravity experimental validation of a planar deployable mechanism
Liu, B., Tan, J., Lyu, H., Zhang, Z., Liu, Z., & Huang, H. L. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.111299 · Cited by 2
Study on the Design Methodology of Turbine Profiles Operating Within Wide-Inflow-Angle Ranges
Zhang, R., Zhang, W., & Lei, C. (2025) · Journal of Propulsion and Power · doi:10.2514/1.b39619 · Cited by 2
Constructing sodium alginate/calcium peroxide/attapulgite composite for controllable release of H2O2 and alkalinity in the aquatic environment: Optimizing via design of experiment (DOE) method and interactions analysis
Liu, Y., Yang, Y., Jin, J., Xiao, Z., Wan, J., & Long, X. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.108281 · Cited by 2
Toward a GPU-enabled billionaire SVD in pyLOM
Miró, A., Eiximeno, B., Gasparino, L., Kutz, N., Rodríguez, I. M., & Lehmkuhl, O. (2026) · Acta Mechanica · doi:10.1007/s00707-025-04621-1 · Cited by 2
Interval model calibration with response-consistent supervised learning network
Zhao, Y., Yun, Q., Bi, S., Wang, X., & Beer, M. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.113942 · Cited by 2
Development of neural network-based surrogate models in geotechnics: a state-of- the-art review
Cerek, K., Dao, D. A., Hadjiloo, E., & Grabe, J. (2026) · Machine Learning and Data Science in Geotechnics · doi:10.1108/mlag-04-2025-0011 · Cited by 2
A modified trust-region assisted variable-fidelity optimization framework for computationally expensive problems
Li, C., & Dong, H. (2022) · Engineering Computations · doi:10.1108/ec-08-2021-0456 · Cited by 2
Design Space Reduction using Multi-Fidelity Model-Based Active Subspaces
Mufti, B., Perron, C., Gautier, R. H., & Mavris, D. N. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3592 · Cited by 2
Optimization of the Elongation of Bioplastic from Durian Seed Starch Using Response Surface Methodology
Handayani, P. A., Devi, A. L., & Ganisha, N. A. (2023) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1203/1/012002 · Cited by 5
“However, to find out an optimum process that is influenced by several variables, a lot of experimental data is needed, which takes a long time and costs a lot of money [24].”
Background
A new approach for data-driven surrogate modelling applied in highly nonlinear engineering functions
Parnianifard, A., Sharma, A., & Chaudhary, S. (2024) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2024.2439488 · Cited by 2
“Some of the relative applications of surrogates in practical engineering problems can be featured in antenna design [34–37], robotics [38], optimal control [39,40], wireless communication [41,42], supply chain management [39,43], aircraft [32,44,45], and robust design under uncertainty [46–48].”
Data-driven modeling for rapid prediction of aerodynamic noise directivity of flow over a cylinder
Jin, Y. H., Liao, F., & Cai, J. (2025) · Applied Mathematical Modelling · doi:10.1016/j.apm.2025.115937 · Cited by 2
Implementation and Assessment of Machine Learning Methods for Multidisciplinary Aerospace Problems
Kilic, D., & Nikbay, M. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-0492 · Cited by 3
A machine learning approach to modelling temperature-dependent cyclic behaviour
Sverdrup, F., Pellegrino, A., Tagarielli, V. L., & Tasdemir, B. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.112369 · Cited by 2
Preliminary Sizing of a Vertical-Takeoff–Horizontal-Landing TSTO Launch Vehicle Using Multidisciplinary Analysis Optimization
Xu, X., Fang, X., & Yu, X. (2025) · Aerospace · doi:10.3390/aerospace12070567 · Cited by 2
Probabilistic Modeling and Uncertainty Quantification of Detailed Combustion Simulation for a Swirl Stabilized Spray Burner
Enderle, B., Rauch, B., Grimm, F., Eckel, G., & Aigner, M. (2023) · Flow Turbulence and Combustion · doi:10.1007/s10494-023-00426-1 · Cited by 2
“Among the various models proposed in the literature (Forrester and Keane 2008; Yondo and Andrés 2018), Polynomial Chaos Expansion (PCE) in combination with sparse quadrature approaches (Le Maître and Knio 2010) have drawn increasing attention for the use in UQ (Najm 2009; Sheen and Wang 2011; de Souza and Colaço 2014).”
Background
AutoEmulate: A Python package for semi-automated emulation
Stoffel, M. A., Li, B. M., Westerling, K., Arana, S., Balmus, M., Daub, E. G., & Niederer, S. (2025) · The Journal of Open Source Software · doi:10.21105/joss.07626 · Cited by 2
“Emulation is becoming increasingly widespread, ranging from engineering (Yondo et al., 2018), architecture (Westermann & Evins, 2019), biomedical (Strocchi et al., 2023) and climate science (Bounceur et al., 2015), to agent-based models (Angione et al., 2022).”
Blade planform improvement and airfoil shape optimization of helicopters in hover flight
Shahmiri, F., & Badihi, M. E. (2020) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-020-02499-9 · Cited by 1
Local Surrogate Modeling for Spatial Emulation of Gas-Turbine Combustion Via Similarity-Based Sample Processing
Geng, J., Qi, H., Li, J., & Wang, X. (2024) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4065994 · Cited by 1
A non-uniform multi-point incremental Kriging facing with discrete response surfaces
Wang, S., Lu, H., & Zhang, Y. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112397 · Cited by 1
Optimal design for a gravity-balanced compliant mechanism
Chau, N. L., Vo, H. X., & Dao, T.-P. (2024) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-024-02190-6 · Cited by 1
“Design of experiments (DOE) methods are utilized to determine a series of enough experiments in numerical analysis or physical test [16].”
Development of Machine Learning Based Surrogate Models Using Fully Coupled 3D Numerical Analysis of Scour around Slab-on-Grade Foundations
Chimauriya, H. R., Biswas, N., Gajurel, A., & Puppala, A. J. (2025) · Geotechnical Frontiers · doi:10.1061/9780784485989.003 · Cited by 1
A physically based Proper Orthogonal Decomposition for structural mechanics involving dissipative processes
Lucarelli, L. C., Blal, N., Platzer, A., Gravouil, A., & Iampietro, D. (2025) · Finite Elements in Analysis and Design · doi:10.1016/j.finel.2025.104386 · Cited by 1
Deep learning-based surrogate model to estimate scour around slab-on-grade foundations subjected to flooding conditions
Chimauriya, H. R., Biswas, N., Puppala, A. J., & Gajurel, A. (2025) · Canadian Geotechnical Journal · doi:10.1139/cgj-2025-0118 · Cited by 1
Assessment of Oscillating Wings to Deliver Air Mass Flow Under Power and Thrust Constraints
Özyılmaz, E. B., & Kaya, M. (2025) · Aerospace · doi:10.3390/aerospace12080740 · Cited by 1
“Through the integration of data-driven insights, machine learning methods like artificial neural networks (ANNs) or support vector regression (SVR) offer reliable means for prediction, optimization, and design enhancement [24].”
A novel adaptive sampling approach with batch selection for the automatic generation of surrogate models in geotechnical engineering
Yang, Y., López, A. R., Tsiampousi, A., & Taborda, D. M. G. (2026) · Data-Centric Engineering · doi:10.1017/dce.2025.10036 · Cited by 1
“…large number of simulations or real-time decision-making (Jones et al., 1998; Jones, 2001), e.g., civil engineering (Asher et al., 2015; Nini ć et al., 2017; Qi and Fourie, 2018; Cao et al., 2020), aerospace engineering (Yondo et al., 2018), and chemical engineering (McBride and Sundmacher, 2019).”
A novel dual-branch graph-based reconstruction framework of thermal fields for orbital satellites
Li, Q., Xu, W., Lv, N., Zhang, S., Zhang, Y., Zhao, X., Xia, Y., & Li, X. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112513 · Cited by 1
Transforming 3D multi-physics simulations into real-time onboard intelligence for energy storage systems thermal management with physics-informed neural networks
Cao, W., Wang, B., Chen, J., Li, X., Dong, T., Ma, Q., & Li, W. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128057 · Cited by 1
Critical Evaluation into the practical utility of the Design of Experiments
Sharma, M. J., & Sharma, S. (2021) · Engineering Management in Production and Services · doi:10.2478/emj-2021-0021 · Cited by 1
“Aviation Space and Environmental Medicine, 67, 235-242.”
Method
Machine Learning based Surrogate Assisted Multi-Objective Optimization of Continuous Casting Process
Inapakurthi, R. K., & Mitra, K. (2021) · Indian Control Conference · doi:10.1109/icc54714.2021.9703180 · Cited by 1
Robustness Analysis on the Aerothermal Performance of Turbine Blade Squealer Tip
Huang, M. N., Li, Z., & Li, J. (2022) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.4054377 · Cited by 1
Research of a novel aerodynamic evaluation method for fixed-wing UAV
Wu, Y., Liu, D., Wang, T., & Zhao, A. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2633/1/012001 · Cited by 1
A Novel Constrained Latin Hypercube Sampling for Electric Machine
Li, J., Tan, N. M. L., Xu, Z., & Fu, W. N. (2023) · IEEE Industrial Electronics and Applications Conference (IEACon) · doi:10.1109/ieacon57683.2023.10370299 · Cited by 1
“These methods primarily take into consideration the randomness and nonrepeatability encountered in field experiments or computer simulations [11].”
Method
PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation
Spinelli, A., & Kipouros, T. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06110 · Cited by 1
“In the computational engineering field, quasi-random sampling is preferred, with Latin Hypercube sampling and Sobol sequences being most used (Yondo et al., 2018).”
Method
Sequential sampling for functional estimation via Sieve
Benevento, A., Ahadi, P., Gupta, S., Pacella, M., & Paynabar, K. (2024) · Quality and Reliability Engineering International · doi:10.1002/qre.3557 · Cited by 1
Bidirectional machine learning–assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization
Hou, Z., Lin, Y., Liu, T., & Lu, W. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33405-8 · Cited by 1
“…learning methods, which are employed as pattern recognition tools to replace the numerical model and avoid com-putation-expensive iterations (Yondo et al. 2018; Luo et al. 2020; Jiang et al. 2021), i.e., intelligent surrogate models are required, while maintaining a high computational…”
Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm
Márquez-Grajales, A., Mezura‐Montes, E., Acosta‐Mesa, H., & Salas-Martínez, F. (2024) · Mathematical and Computational Applications · doi:10.3390/mca29050078 · Cited by 1
“The solutions were randomly initialized within the search space following a uniform distribution [21,63–65].”
Compression resistance of horsetail-inspired porous thin-walled structures
Xie, B., Xie, X., Yang, Y., Han, L., Yan, F., & Lu, Z. (2025) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/14644207251329490 · Cited by 1
“31,32 Using the BBD experimental design and simulation data, 33 multiple regression ﬁ tting was applied to the HH-type structure, establishing the functional relationship between each response and related factors, as follows: The type HHFH structure was ﬁ tted with multiple regression, as follows:…”
The Design of Experiments as a Methodological Framework for the Improvement of Manufacturing Processes
Nakasima-López, S., Nakasima-López, M. O., Estrada, K. F. M., & Salomón, E. B. (2019) · Advances in chemical and materials engineering book series · doi:10.4018/978-1-7998-1518-1.ch012 · Cited by 1
Mode Pursuing Sampling Method Using Coordinate Perturbation for High-dimensional Expensive Black-box Optimization
Wu, Y., Long, T., Shi, R., & Wang, G. G. (2019) · AIAA AVIATION Forum · doi:10.2514/6.2019-3667 · Cited by 1
Retracted
Number of DoE Required for Estimating Different Quality Surrogate
Yin, L., Wang, S., Lv, L., Song, X., Guo, Z., & Sun, W. F. (2021) · International Journal of Computational Methods · doi:10.1142/s021987622141019x · Cited by 1
Introduction
Koziel, S. M., & Pietrenko‐Dabrowska, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-38926-0_1 · Cited by 1
Surrogate modelling for high-lift multi-element hydrofoil shape optimization of a hydrokinetic turbine blade
Rubio-Clemente, A., Aguilar, J., & Chica, E. (2020) · Renewable Energy and Power Quality Journal · doi:10.24084/repqj18.258 · Cited by 1
"Review on Surrogate-Based Global Optimization with Biomedical Applications"
Haridy, A. (2023) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2023.53.008409 · Cited by 1
Common kernel-smoothed proper orthogonal decomposition (CKSPOD): An efficient reduced-order model for emulation of spatiotemporally evolving flow dynamicsPreprint
Chang, Y., Wang, X., Zhang, L., Li, Y., Mak, S., Wu, C. F. J., & Yang, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08893 · Cited by 1
“The results are consistent with the previous finding that the CPOD method can accurately capture major performance metrics, such as time-averaged liquid film thickness and spreading angle, but not detailed flow dynamics [10,11].”
BackgroundResult
Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and Gas-to-LiquidsPreprint
Hao, Z., Zhang, C., & Lapkin, A. A. (2021) · doi:10.33774/chemrxiv-2021-v88c7 · Cited by 1
Non-intrusive reduced order modelling for aerodynamic applications
Balla, K. (2021) · doi:10.23889/suthesis.58705 · Cited by 1
Addressing the Limitations of Accident Scenario Identifications with Respect to Diverse Manual Responses Affecting the Progression of an Initiating EventPreprint
Park, J., & Kim, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4480466 · Cited by 1
Pylom: A Hpc Open Source Reduced Order Model Suite for Fluid Dynamics ApplicationsPreprint
Eiximeno, B., Miró, A., Begiashvili, B., Valero, E., Rodriguez, I., & Lehmkuhl, O. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4856967 · Cited by 1
Self-citation
A hybrid analytical-numerical algorithm for determining the neuronal current via EEGPreprint
Hashemzadeh, P., FOKAS, A. S., & Schönlieb, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01403
“Regression Model of us(r, τ , τ̂ ) For A Realistic Head Model Regression models constructed using data generated by PDE solvers are referred to as surrogate models [18, 19, 20, 21, 22, 23].”
Method
Adaptive Experimental Design for Aerodynamic Modeling with Hard-to-Change Factors
Choi, U., Sung, T., & Ahn, J. (2020) · Journal of Aerospace Information Systems · doi:10.2514/1.i010813
A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical SystemPreprint
Parnianifard, A., Wuttisittikulkij, L., Zemouche, A., & Phanomchoeng, G. (2020) · doi:10.21203/rs.3.rs-52777/v1
“However, several studies have systematically illustrated the applications of surrogate-based optimization algorithms [36], [37], [41]– [43].”
Background
A novel ISLE-ESE method for mixed-variable experiment design
Yang, J., Wu, Z., Wang, W., li, J., Zhang, W. H., Jinhua, S., Zhao, H. L., Wen, Q., & Zhen, X. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1043/2/022043
“, the input based space-filling design and output based design([6]).”
Background
Acquisition of Swept Aerodynamic Data by the Consecutive Changing of Wing Model Configuration in a Wind Tunnel Test Using Remote Feedback Control
Wakimoto, K., Chiba, K., Kato, H., & Nakakita, K. (2021) · Aerospace · doi:10.3390/aerospace8080217
“Furthermore, dimensionality reduction for aerodynamic data acquired by WTT [4] must work better when the data set is generated consecutively/automatically rather than discrete/manually, regardless of any data reduction methods.”
Method
Robust Optimal Placement and Sensitivity Analysis of Multiple Transmitting Antennas Under Uncertainty: A New Surrogate-Based Evolutionary AlgorithmPreprint
Parnianifard, A., Mumtaz, S., Chaudhary, S., Imran, M. A., & Wuttisittikulkij, L. (2021) · Research Square · doi:10.21203/rs.3.rs-976342/v1
“To handle this, Kriging surrogate (also known as the Gaussian process) has been applied for different types of engineering design problems [23]–[25].”
Method
Aerodynamic Optimisation Based on Stability and Sensitivity Analysis
Wang, Y. (2020) · doi:10.20868/upm.thesis.65920
Knowledge-Driven Adaptive Sequential Modeling and Prediction of Aerodynamic Characteristics
Pu, X., Hao, J., Zhang, F., He, S., & Long, Y. (2022) · Mechanisms and machine science · doi:10.1007/978-981-16-7381-8_91
Surrogate modelling and robust optimization of multi-stage metal forming processes
de Gooijer, B. M. (2022) · doi:10.3990/1.9789036554152
A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems
Tan, J., Chen, H., Ye, X., & Lin, Y. (2022) · Energies · doi:10.3390/en15134826
Augmenting covariance estimation for ensemble-based data assimilation in multiple-query scenarios
Ilersich, A. F., Schau, K. A., Oefelein, J. C., Steinberg, A. M., & Yano, M. (2022) · Combustion Theory and Modelling · doi:10.1080/13647830.2022.2105259
“This family may contain many different but closely related conﬁgurations [24], or it may contain a single conﬁguration undergoing relatively slow changes in operating conditions, as in the digital twin conﬁguration [3].”
Background
A multiobjective portfolio optimization for energy assets using D-Optimal design and mixture design of experiments
Leal, G. D. S., Romão, E. L., dos Reis, D. L. D. P. E., Balestrassi, P. P., & de Paiva, A. P. (2022) · Production · doi:10.1590/0103-6513.20210119
“According to (Yondo et al., 2018), industrial experiments have been focusing mainly on 2-levels and 3-levels designs, resulting in 2 and 3 experiments, respectively.”
Background
Experimental analysis and multidimensional modeling of water condensation due to low-pressure exhaust gas recirculation activation during engine cold starts
Torres, F. M. (2022) · doi:10.4995/thesis/10251/187990
Introduction
Pietrenko‐Dabrowska, A., & Koziel, S. M. (2023) · Springer eBooks · doi:10.1007/978-3-031-43845-5_1
Fast Prediction of Thermal Behaviour of Lithium-ion Battery Energy Storage Systems Based on Meshless Surrogate Model
Wang, B., Cao, W., Dong, T., Zhen, X., Liu, J., Sun, Y., & Zhou, L. (2023) · International Conference on Smart Grid and Smart Cities (ICSGSC) · doi:10.1109/icsgsc59580.2023.10319219
“In order to address this problem, the surrogate model was developed[6].”
BackgroundMethodInfluential
Adaptive Elliptic Basis Function Model Construction Strategy Based on Particle Swarm Optimization
Lin, S., Rong, B., & Yang, S. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-8048-2_172
Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim
Abdelrehim, A., Gandhi, D., Yalburgi, S., Bharambe, A., Anantharaman, R., & Rackauckas, C. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-2323
SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration
Robani, M. D., Saves, P. M. V., Palar, P. S., Zuhal, L., & Morlier, J. (2025) · AIAA SCITECH 2025 Forum · doi:10.2514/6.2025-0777 · Cited by 5
“Surrogate models are commonly employed to efficiently address multi-query tasks [2].”
A Novel Ann-Based Approach for Simultaneous Selective Dimension Reduction and Surrogate Modeling. Application to Aerodynamic Shape OptimizationPreprint
Moussaoui, Z., karafi, Y., & Majd, B. A. E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5159267
Efficient and Accurate Surrogate-Assisted Approach to Multi-objective Optimization Using Deep Neural Networks
Yang, Y., & Sato, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-3538-2_12
Sparse Sampling and Fusion of Multi-Fidelity Aerodynamic Heating Data
Nie, Y., Yang, C., Wei, M., Jiang, X., & Ma, X. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906155
Study on bicycle stability and self-stabilization mechanism by dynamic-and-data-driven surrogate modeling
Miao, X., & Liu, C. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11524-2 · Cited by 1
Exploring Spanwise Loss Patterns: Numerical Analysis and Modeling of Linear Cascades with Tip Clearance Based on Leakage Flow Intensity
Ding, Y., Jin, D., Zhang, J., & Guo, H. (2025) · Journal of Aerospace Engineering · doi:10.1061/jaeeez.aseng-6156
Global parameter sensitivity analysis using Sobol and extended Fourier amplitude sensitivity test methods for associated gas injection in the shale oil reservoir
Xia, Z., Yin, H., Wang, X., & Fu, J. (2025) · Physics of Fluids · doi:10.1063/5.0285808
Computationally‐Efficient Environmental and Economic Multi‐Objective Optimization of a Methanol Production Process via Surrogate Modeling
Sánchez, L. F., Vallerio, M., & Manenti, F. (2025) · Engineering Reports · doi:10.1002/eng2.70422
“The maxmin LHS is a space-filling DoE based on the LHS, which aims to improve the spatial distribution of samples [8].”
Influential
Improving Prediction Accuracy and Robustness in Injection Mechanism Based on Simplified Pareto and Updated Training Set Hybrid Metamodel
You, D., Zheng, S., Li, F., & Luo, X. (2025) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp9110358
“Yondo et al. compared the kriging and response surface models and found that, although the high-order response surface is applicable to highly nonlinear problems, it needs a large training set and thus has an unstable performance [19].”
MLPA-GASA: A Novel Surrogate-Assisted Hybrid Algorithm for Wind Farm Layout Optimization
Shang, W., Gong, L., Liu, X., Liu, M., & Xiang, X. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-1252-2_22
Efficient Aerodynamic Surrogate Modeling of Switchblade UAVs for Digital Twin
Lyu, H., & Cao, R. (2025) · International Conference on Robotics, Control and Automation Engineering (RCAE) · doi:10.1109/rcae66389.2025.11355242
“Sampling the design space, that is, selecting a good set of data points, is crucial for simulation, with the main goal of obtaining the maximum amount of information from a limited number of samples [6].”
Multi-fidelity surrogate modeling with low-fidelity auxiliary sampling for fluid flow prediction
Yang, C., Hai, C., Tang, H., & Mei, L. (2026) · Physics of Fluids · doi:10.1063/5.0312674
A multi-strategy improved marine predator algorithm for large-scale optimization
Liu, B., Ren, W., Li, H., Liang, Y., Li, J., & Gao, L. (2026) · International Conference on Communication Networks and Machine Learning (CNML) · doi:10.1109/cnml68938.2026.11453131
“For instance, in aircraft design[1], it is essential to optimize dimensions and configurations to enhance performance, while in robotic path planning and control [2], finding optimal trajectories is crucial.”
Surrogate models coupled with machine learning to approximate complex physical phenomena involving aerodynamic and aerothermal simulations
Dupuis, R. (2019) · doi:10.70675/71187689zde88z4549z81d4z14f2c5bf7cd7
Data assimilation with reduced basis and noisy measurement : Applications to nuclear reactor cores
Gong, H. (2018) · doi:10.70675/0ae0fc95z5f38z4025zad3azd5d96ad9545a
“A favoured application of ROM is in aerodynamic analyses [217, 212].”
Method
Étude du comportement de l'alizarine comme molécule redox dans une batterie à flux dans une solution aqueuse basique et dans un mélange aqueux comprenant un solvant naturel
Bassil, P. (2021) · theses.fr (ABES) · doi:10.70675/9573f320zdf2dz482czae79z72468b90b3b1
Adaptive full-field multi-fidelity surrogate based optimization dedicated to turbomachinery design
Khatouri, H. (2021) · doi:10.70675/883f3dbdz8428z46f3z9ff3zb369fc6b2e11
An efficient stress prediction approach for Diverless Unified Support Tube of FPSO based on high-fidelity simulation and LSTM
Wang, B., Zhong, L., Meng, Z., Wu, Y., & Shu, L. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125449
Projection-based model reduction using Pressio: application to hypersonic aerothermodynamics
Rizzi, F., Parish, E., Blonigan, P., Tencer, J. T., Wentland, C. R., Kurzawski, A. J., Heyningen, R. L. V., Schilly, C., & Brunini, V. (2026) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-026-04300-2
“Typical many-query problems require thousands of model evaluations to characterize the effect of uncertainties in parameters and operating conditions on the system response (Yondo et al. 2018; Kelley 1999).”
Design, optimization, and performance validation of a subsonic wind tunnel for aerodynamic investigations
Jalal, A., Hassan, W. U., Akram, U., Buzal, M. Z., Malik, M. A. I., Asim, A., Asghar, H., Jamil, S. R., & Usman, M. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100652
A sequential design of experiments for dual assessment of performance and safety boundaries in conceptual design
Wang, Y., Baoqing, Y. A. N. G., Li, P., Wang, S., & Ma, D. J. (2026) · Applied Mathematical Modelling · doi:10.1016/j.apm.2026.117047
Surrogate-Assisted Optimization of Hypersonic Gliding Vehicle for Range Extension in Re-entry Flight
Yixing, S., Shi, R., & Tai, X. (2026) · Mechanisms and machine science · doi:10.1007/978-981-95-7342-4_35
Time-varying aeroelastic analysis coupled with flight dynamics of a flying-wing aircraft during the morphing process
Liu, J., Qian, W., Zhao, Y., Hu, X., & Ai, X. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112709
A Preliminary Analysis of a Physics-Informed Neural Network for the Forward Problem in EEG
FOKAS, A. S., Aghaei, A. A., & Hashemzadeh, P. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6040042
Industrial CFD and Fluid Modeling in Engineering: Third Edition
Vanna, F. D. (2026) · Fluids · doi:10.3390/fluids11070174
“The aerodynamics are resolved with three-dimensional RANS computations of the NREL Phase VI rotor using the transition SST k – ω model [36,37], and the deflector geometry is optimized through a Box–Behnken response-surface model whose quadratic fit attains an R 2 of 0.98 [3,38].”
Influential
Reduced Multifidelity Low-Sonic-Boom Design for Supersonic Transport by Manifold Alignment
Xiao, L., Chao, S., Zhu, Z., Hongyang, L., Yu, T., & Tao, T. (2026) · Journal of Aircraft · doi:10.2514/1.c039004
Surrogate-based multi-objective optimization of reactor vessel radiative fin geometry for a PRISM-like RVACS configuration
Benson, T., Kim, H., Seo, J., & Hassan, Y. A. (2026) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2026.115143

Authors: Yondo, R., Andrés, E., & Valero, E.
Affiliations: Universidad Politécnica de Madrid (ES)
Ingeniería de Sistemas para la Defensa de España (ES)
Corresponding author: Yondo, Raul
ORCID: Andrés, Esther
Valero, Eusebio
Venue: Progress in Aerospace Sciences
Publisher: Elsevier BV
Published: 2018-01
Volume: 96
Pages: 23-61
ISSN: 0376-0421 (ISSN-L)
DOI: 10.1016/j.paerosci.2017.11.003
OpenAlex: W2776493547
Cited by: 311
References: 548
Citations by year: 2026: 29 · 2025: 50 · 2024: 44 · 2023: 32 · 2022: 49 · 2021: 41 · 2020: 39 · 2019: 26 · 2018: 1
FWCI: 13.56
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Progress in Aerospace Sciences (publishedVersion)
Funders: European Commission Seventh Framework Programme (FP7-PEOPLE-2013-ITN, 608087)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Physical Sciences › Engineering › Computational Mechanics › Probabilistic and Robust Engineering Design
Physical Sciences › Engineering › Computational Mechanics › Computational Fluid Dynamics and Aerodynamics
Keywords: surrogate models, aircraft aerodynamic design, surrogate-based optimization, experimental design, sampling strategy, dimensionality reduction, sensitivity analysis, uncertainty quantification, multi-fidelity modeling, civil transport aircraft, computational complexity reduction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks