1. NCCU Academic Hub
  2. 學術產出
  3. 商學院
  4. 期刊論文
  5. Item

學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 Locating Infinite Discontinuities in Computer Experiments
作者 洪英超
Hung, Y.C.
Michailidis, G.
Lok, H.P.H.
貢獻者 統計系
關鍵詞 Computer experiments ; Infinite discontinuity ; Active learning ; Support vector machines ; Quasi-Monte Carlo methods
日期 2020-05
上傳時間 22-一月-2021 09:15:58 (UTC+8)
摘要 Identification of input configurations so as to meet a prespecified output target under a limited experimental budget has been an important task for computer experiments. Such a task often involves the development of response models and design of experimental trials that rely on the models exhibiting continuity and differentiability properties. Motivated by two canonical examples in systems and manufacturing engineering, we propose a strategy for locating the boundary of the response surface in computer experiments, wherein on one side the response is finite, whereas on the other side it is infinite, leveraging ideas from active learning and quasi-Monte Carlo methods. The strategy is illustrated on an example from computer networks engineering and one from precision manufacturing and shown to allocate experimental trials in a fairly effective manner. We conclude by discussing extensions of the proposed strategy to characterize other types of output discontinuity or nondifferentiability in high-cost experiments, including jump discontinuities in the target output response or pathological structures such as kinks and cusps.
關聯 SIAM/ASA Journal on Uncertainty Quantification, Vol.8, No.2, pp.717-747
資料類型 article
DOI https://doi.org/10.1137/18M1209076
dc.contributor 統計系
dc.creator (作者) 洪英超
dc.creator (作者) Hung, Y.C.
dc.creator (作者) Michailidis, G.
dc.creator (作者) Lok, H.P.H.
dc.date (日期) 2020-05
dc.date.accessioned 22-一月-2021 09:15:58 (UTC+8)-
dc.date.available 22-一月-2021 09:15:58 (UTC+8)-
dc.date.issued (上傳時間) 22-一月-2021 09:15:58 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/133660-
dc.description.abstract (摘要) Identification of input configurations so as to meet a prespecified output target under a limited experimental budget has been an important task for computer experiments. Such a task often involves the development of response models and design of experimental trials that rely on the models exhibiting continuity and differentiability properties. Motivated by two canonical examples in systems and manufacturing engineering, we propose a strategy for locating the boundary of the response surface in computer experiments, wherein on one side the response is finite, whereas on the other side it is infinite, leveraging ideas from active learning and quasi-Monte Carlo methods. The strategy is illustrated on an example from computer networks engineering and one from precision manufacturing and shown to allocate experimental trials in a fairly effective manner. We conclude by discussing extensions of the proposed strategy to characterize other types of output discontinuity or nondifferentiability in high-cost experiments, including jump discontinuities in the target output response or pathological structures such as kinks and cusps.
dc.format.extent 2113345 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) SIAM/ASA Journal on Uncertainty Quantification, Vol.8, No.2, pp.717-747
dc.subject (關鍵詞) Computer experiments ; Infinite discontinuity ; Active learning ; Support vector machines ; Quasi-Monte Carlo methods
dc.title (題名) Locating Infinite Discontinuities in Computer Experiments
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1137/18M1209076
dc.doi.uri (DOI) https://doi.org/10.1137/18M1209076