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| 불확실성 정량화× | 대리 모델 기반 최적화× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 최적화 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Seminal modern form: 2002 | 1989 (computer experiments formulation) |
| 창시자≠ | Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002) | Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963) |
| 유형≠ | Computational uncertainty analysis framework | Metamodel-assisted black-box optimization |
| 원전≠ | Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644. DOI ↗ | Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley. link ↗ |
| 별칭≠ | UQ, polynomial chaos expansion, PCE, Kriging surrogate | Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization |
| 관련≠ | 9 | 5 |
| 요약≠ | Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs. | Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses. |
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