000004932 001__ 4932
000004932 005__ 20141119144605.0
000004932 04107 $$aeng
000004932 046__ $$k2002-06-02
000004932 100__ $$aGhiocel, Dan M.
000004932 24500 $$aSTOCHASTIC FIELD MODELS FOR APPROXIMATING HIGHLY NONLINEAR RANDOM RESPONSES

000004932 24630 $$n15.$$pProceedings of the 15th ASCE Engineering Mechanics Division Conference
000004932 260__ $$bColumbia University in the City of New York
000004932 506__ $$arestricted
000004932 520__ $$2eng$$aThe paper discusses different stochastic field models for approximating highly nonlinear responses. A key requirement for a good stochastic modeling is to be intimately related to the physics of the problem. Simple approximation procedures that have been popular in the past, may not work for many real-life problems. Classical Response Surface Method (RSM) is often insufficiently accurate for applications that involve highly nonlinear relationships. The paper describes different stochastic field modeling techniques that based on the author’s experience are adequate for high-complexity applications. These techniques are: (i) stochastic field expansion techniques, (ii) stochastic field interpolation techniques and (iii) stochastic localaveraging expansion techniques. Each category of these stochastic techniques has advantages and disadvantages that the analyst should understand before using them for a real application. 

000004932 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000004932 653__ $$aStochastic fields, response surface, highly nonlinear systems, stochastic expansions

000004932 7112_ $$a15th ASCE Engineering Mechanics Division Conference$$cNew York (US)$$d2002-06-02 / 2002-06-05$$gEM2002
000004932 720__ $$aGhiocel, Dan M.
000004932 8560_ $$ffischerc@itam.cas.cz
000004932 8564_ $$s419249$$uhttp://invenio.itam.cas.cz/record/4932/files/576.pdf$$yOriginal version of the author's contribution as presented on CD, .
000004932 962__ $$r4594
000004932 980__ $$aPAPER