Design for ICME: A Data-Driven Decision Support Framework for Quantifying and Managing Uncertainty
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OpenAlex topics: Business Process Modeling and Analysis · Advanced Multi-Objective Optimization Algorithms · Constraint Satisfaction and Optimization
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References (69)
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1. Olson GB (1997) Computational design of hierarchically structured materials. Science 277(5330):1237–124210.1126/science.277.5330.1237 ↗
2. Arróyave R, McDowell DL (2019) Systems approaches to materials design: past, present, and future. Annu Rev Mater Res 49(1):103–12610.1146/annurev-matsci-070218-125955 ↗
3. Mistree F, Smith W, Bras B, Allen JK, Muster D (1990) Decision-based design: a contemporary paradigm for ship design. Trans Soc Naval Archit Mar Eng 98:565–597
4. Mistree F, Hughes O, Bras B (1993) The compromise decision support problem and the adaptive linear programming algorithm. Prog Astronaut Aeronaut 150:251–251
5. Mistree F, Allen JK, Woodruff GW, Simon H (1997) Position paper optimization in decision-based design. Optimization in industry. Palm Coast, FL
6. Senn M (2015) An integrated surrogate modeling approach for materials and process design. In: Proceedings of the 3rd world congress on integrated computational materials engineering (ICME 2015), pp 331–33810.1002/9781119139508.ch39 ↗
7. van Beek A, Nevile Karkaria V, Chen W (2023) Digital twins for the designs of systems: a perspective. Struct Multidiscip Optim 66(3):4910.1007/s00158-023-03488-x ↗
8. Rasmussen CE, Williams CK (2006) Gaussian processes for machine learning, MIT Press, Cambridge
9. Simpson T, Toropov V, Balabanov V, Viana F (2008) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come-or not. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, MAO. p 580210.2514/6.2008-5802 ↗
10. Panchal JH, Kalidindi SR, McDowell DL (2013) Key computational modeling issues in integrated computational materials engineering. Comput Aided Des 45(1):4–2510.1016/j.cad.2012.06.006 ↗
11. Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2015) Taking the human out of the loop: a review of bayesian optimization. Proc IEEE 104(1):148–17510.1109/JPROC.2015.2494218 ↗
12. Frazier PI (2018) Bayesian optimization. In; Recent advances in optimization and modeling of contemporary problems. Informs, pp 255–27810.1287/educ.2018.0188 ↗
13. Frazier PI (2018) A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811
14. Choi H-J, Austin R, Allen JK, McDowell DL, Mistree F, Benson DJ (2005) An approach for robust design of reactive power metal mixtures based on non-deterministic micro-scale shock simulation. J Comput Aided Mater Des 12(1):57–8510.1007/s10820-005-1056-1 ↗
15. Nellippallil AB, Mohan P, Allen J, Mistree F (2020) An inverse, decision-based design method for robust concept exploration. J Mech Des 142(8):08170310.1115/1.4045877 ↗
16. Chen W, Simpson T, Allen J, Mistree F (1999) Satisfying ranged sets of design requirements using design capability indices as metrics. Eng Optim 31:615–63910.1080/03052159908941389 ↗
17. McDowell DL, Panchal J, Choi H-J, Seepersad C, Allen J, Mistree F (2009) Integrated design of multiscale, multifunctional materials and products. Butterworth-Heinemann
18. Allen J, Panchal, J, Mistree, F, Singh, A, Gautham B (2015) Uncertainty management in the integrated realization of materials and components. In: Proceedings of the 3rd world congress on integrated computational materials engineering (ICME 2015), pp 339–34610.1002/9781119139508.ch40 ↗
19. Kalidindi S, Niezgoda S, Landi G, Vachhani S, Fast T (2010) A novel framework for building materials knowledge systems. Comput Mater Contin 17:103–125
20. Kalidindi S, Niezgoda S, Salem A (2011) Microstructure informatics using higher-order statistics and efficient data-mining protocols. JOM 63:34–4110.1007/s11837-011-0057-7 ↗
21. Ghosh S, Anantha Padmanabha G, Peng C, Andreoli V, Atkinson S, Pandita P, Vandeputte T, Zabaras N, Wang L (2021) Inverse aerodynamic design of gas turbine blades using probabilistic machine learning. J Mech Des 144(2):021706
22. Sui F, Guo R, Zhang Z, Gu GX, Lin L (2021) Deep reinforcement learning for digital materials design. ACS Mater Lett 3(10):1433–143910.1021/acsmaterialslett.1c00390 ↗
23. Kim B, Lee S, Kim J (2020) Inverse design of porous materials using artificial neural networks. Sci Adv 6(1):eaax932410.1126/sciadv.aax9324 ↗
24. Chen CT, Gu GX (2020) Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci 7(5):190260710.1002/advs.201902607 ↗
25. Muster D, Mistree F (1988) The decision support problem technique in engineering design. Int J Appl Eng Educ 4:23–33
26. Simon HA (1997) Administrative behavior: a study of decision-making processes in administrative organizations. Free Press, New York
27. Simon HA (1996) Designing organizations for an information-rich world. Int Libr Crit Writ Econ 70:187–202
28. Sinha A, Bera N, Allen JK, Panchal JH, Mistree F (2013) Uncertainty management in the design of multiscale systems. J Mech Des 135(1):01100810.1115/1.4006186 ↗
29. Allen JK, Seepersad C, Choi H, Mistree F (2006) Robust design for multiscale and multidisciplinary applications. J Mech Des 128(4):832–84310.1115/1.2202880 ↗
30. Chen W, Baghdasaryan L, Buranathiti T, Cao J (2004) Model validation via uncertainty propagation and data transformations. AIAA J 42(7):1406–141510.2514/1.491 ↗
31. Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. John Wiley & Sons10.1002/9780470770801 ↗
32. Bostanabad R, Kearney T, Tao S, Apley DW, Chen W (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int J Numer Methods Eng 114(5):501–51610.1002/nme.5751 ↗
33. Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc: Ser B (Stat Methodol) 63(3):425–46410.1111/1467-9868.00294 ↗
34. Arendt PD, Apley DW, Chen W (2012) Quantification of model uncertainty: calibration, model discrepancy, and identifiability. J Mech Des 134(10):10090810.1115/1.4007390 ↗
35. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscip Optim 23(1):1–1310.1007/s00158-001-0160-4 ↗
36. Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–423
37. Tao S, Shintani K, Bostanabad R, Chan Y-C, Yang G, Meingast H, Chen W (2017) Enhanced Gaussian process metamodeling and collaborative optimization for vehicle suspension design optimization. In: Proc. International design engineering technical conferences and computers and information in engineering conference, vol 58134. American Society of Mechanical Engineers, p V02BT03A03910.1115/DETC2017-67976 ↗
38. Simpson T, Mistree F, Korte J, Mauery T (1998) Comparison of response surface and kriging models for multidisciplinary design optimization. In: Proc. 7th AIAA/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization. p 475510.2514/6.1998-4755 ↗
39. Simpson TW, Mauery TM, Korte JJ, Mistree F (2001) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J 39(12):2233–224110.2514/2.1234 ↗
40. Mera N (2007) Efficient optimization processes using kriging approximation models in electrical impedance tomography. Int J Numer Methods Eng 69(1):202–22010.1002/nme.1772 ↗
41. Wang G, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129(4):370–38010.1115/1.2429697 ↗
42. Farhang-Mehr A, Azarm S (2005) Bayesian meta-modelling of engineering design simulations: a sequential approach with adaptation to irregularities in the response behaviour. Int J Numer Meth Eng 62(15):2104–212610.1002/nme.1261 ↗
43. Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–49210.1023/A:1008306431147 ↗
44. Brochu E, Cora VM, De Freitas N (2010) A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprintarXiv:1012.2599
45. Scott W, Frazier P, Powell W (2011) The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression. SIAM J Optim 21(3):996–102610.1137/100801275 ↗
46. Wu J, Frazier P (2016) The parallel knowledge gradient method for batch bayesian optimization. In: Advances in neural information processing systems, vol 29
47. Močkus J (1974) On Bayesian methods for seeking the extremum. In: Proc. IFIP technical conference on optimization techniques. Springer, pp 400–40410.1007/978-3-662-38527-2_55 ↗
48. Kushner HJ (1964) A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J Basic Eng 86(1):97–10610.1115/1.3653121 ↗
49. Srinivas N, Krause A, Kakade SM, Seeger M (2009) Gaussian process optimization in the bandit setting: no regret and experimental design arXiv preprint arXiv:0912.3995
50. Hennig P, Schuler CJ (2012) Entropy search for information-efficient global optimization. J Mach Learn Res 13(1):1809–1837
51. Hernández-Lobato JM, Hoffman MW, Ghahramani Z (2014) Predictive entropy search for efficient global optimization of black-box functions. In: Advances in neural information processing systems, vol 27
52. Tao S, Van Beek A, Apley DW, Chen W (2021) Multi-model bayesian optimization for simulation-based design. J Mech Des 143(11):11170110.1115/1.4050738 ↗
53. Schonlau M, Welch WJ, Jones DR (1998) Global versus local search in constrained optimization of computer models. In: Lecture notes-monograph series. pp 11–25.10.1214/lnms/1215456182 ↗
54. Gardner JR, Kusner MJ, Xu ZE, Weinberger KQ, Cunningham JP (2014) Bayesian optimization with inequality constraints. In: Proc. ICML, vol 2014. pp 937–945
55. Gramacy RB, Gray GA, Le Digabel S, Lee HK, Ranjan P, Wells G, Wild SM (2016) Modeling an augmented Lagrangian for blackbox constrained optimization. Technometrics 58(1):1–1110.1080/00401706.2015.1014065 ↗
56. Picheny V A stepwise uncertainty reduction approach to constrained global optimization. In: Proc. artificial intelligence and statistics. PMLR, pp 787–795
57. Gelbart MA, Snoek J, Adams RP (2014) Bayesian optimization with unknown constraints. arXiv preprint arXiv:1403.5607
58. Ghasemzadeh M, Alam Digonta HMD, Nellippallil AB, Beek A (2025) A comparative analysis of robust and reliable designs using the compromise decision support problem: a case study in hot rod rolling processes. In: ASME design automation conference, paper no. DETC2025–16888510.1115/DETC2025-168885 ↗
59. Ghasemzadeh M, van Beek A NOSTRA: a noise-resilient and sparse data framework for trust region based multi-objective bayesian optimization. In: ASME design automation conference, paper no. DETC2025-16886510.1115/DETC2025-168865 ↗
60. McHutchon A, Rasmussen C (2011) Gaussian process training with input noise. Adv Neural Inf Process Syst 24:1341–1349
61. Lambe AB, Martins JR (2012) Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes. Struct Multidiscip Optim 46:273–28410.1007/s00158-012-0763-y ↗
62. Lin Y, Krishnapur K, Mistree F (1999) Robust design: goal formulations and a comparison of metamodeling methods. In: ASME design automation conference, paper no. DETC99/DAC-860810.1115/DETC99/DAC-8608 ↗
63. Taguchi G (1993) Robust technology development. Mech Eng-CIME 115(3):60–63
64. Chen W (1995) A robust concept exploration method for configuring complex systems. Georgia Institute of Technology
65. Chen W, Allen JK, Tsui K-L, Mistree F (1996) A procedure for robust design: minimizing variations caused by noise factors and control factors. J Mech Des 118(4):478–48510.1115/1.2826915 ↗
66. Kuziak R, Cheng Y-W, Glowacki M, Pietrzyk M (1997) Modeling of the microstructure and mechanical properties of steels during thermomechanical processing. In: NIST Technical Note (USA), vol 139310.6028/NIST.TN.1393 ↗
67. Hodgson PD, Gibbs RK (1992) A mathematical model to predict the mechanical properties of hot rolled C-Mn and microalloyed steels. ISIJ Int 32(12):1329–133810.2355/isijinternational.32.1329 ↗
68. Lee Y, Choi S, Hodgson P (2002) Integrated model for thermo-mechanical controlled process in rod (or bar) rolling. J Mater Process Technol 125–126:678–68810.1016/S0924-0136(02)00347-3 ↗
69. Gladman T, Pickering FB (1972) Some aspects of the structure-property relationships in high-carbon ferrite-pearlite steels. J Iron Steel Inst 210(12):916–930