... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 09 08 2023 20 10 2023 26 10 2023 16 11 2023 design automation metamodeling simulation-based design uncertainty analysis Accepted Manuscript Not Copyedited...

... reliability in design robust design simulation-based design systems design uncertainty analysis uncertainty modeling Office of Naval Research 10.13039/100000006 N000142212459 Email: azarm@umd.edu Email: indranil.hazra92@gmail.com Email: achatter@umd.edu Email: jmsouthg...

... reliability in design uncertainty analysis uncertainty modeling Rare events are events that have low probabilities of occurring (e.g., p ≤ 10 − 4 ), but are significant enough to justify an accurate study. For example, in the engineering design process of structures, assessing the probability...

... uncertainty analysis The values of initial sample points, the added sample points in the iteration process and the corresponding changes of failure probability compared with the RBF-HLA [ 55 ] and MCS are listed in Table 4 and shown in Fig. 5 . It can be found that the RBF-HLA algorithm still fails...

... of ASME for publication in the J ournal of M echanical D esign . 06 12 2022 15 03 2023 17 03 2023 17 05 2023 evidence theory reliability analysis deep learning stacked autoencoders uncertainty analysis Due to limitations in measurement technologies...

... for publication in the J ournal of M echanical D esign . 13 06 2022 21 12 2022 31 12 2022 24 01 2023 design optimization time- and space-dependent reliability analysis uncertainty analysis Engineers always encounter uncertainty in material properties, part dimensions...

... accuracy evidence theory correlation epistemic uncertainty industrial robot kinematics reliability in design robot kinematics uncertainty analysis National Natural Science Foundation of China 10.13039/501100001809 51975199 As one of the effective means to improve working...

... Gaussian processes calibration multiscale simulations reduced-order model microstructural uncertainties machine learning metamodeling multiobjective optimization uncertainty analysis Multiscale models are increasingly employed to quantify the effects of manufacturing-induced microscopic...

... mapping design methodology metamodeling reliability in design uncertainty analysis National Research Foundation of Korea 10.13039/501100003725 2021K2A9A2A06044278 FY2021 Over the past decades, reliability-based design optimization (RBDO) has been widely used for engineering...

... of M echanical D esign . 11 02 2022 01 07 2022 09 07 2022 08 08 2022 reliability analysis importance sampling hybrid Monte Carlo Kriging Gaussian mixture model reliability in design uncertainty analysis National Key Research and Development Program...

... machine learning risk-based design simulation-based design uncertainty analysis uncertainty modeling As an emerging technique, autonomous ground vehicles (AGVs) have gained much attention in different applications in recent years, since they can increase traffic efficiency, reduce...

... limited data Mahalanobis distance design validation uncertainty analysis uncertainty modeling National Natural Science Foundation of China 10.13039/501100001809 52111540267 State Key Laboratory of Fluid Power and Mechatronic Systems 10.13039/501100011310 SKLoFP_ZZ_2102...

... 07 2022 machine learning metamodeling reliability in design uncertainty analysis National Natural Science Foundation of China 10.13039/501100001809 51875525 51905475 52111540267 Natural Science Foundation of Zhejiang Province 10.13039/501100004731 LY21E050008...

... and implement uncertainty analysis under different uncertainty levels (UL) and distributions. Then, with the use of a set of orthogonal polynomials for uniform and Gaussian distribution, respectively, the high dimensional model representation of the mapping relationship is approximately expressed...

... variability. A virtual representation of the LPBF process is first constructed using a physics-based model. To enable faster computation required in uncertainty analysis and decision-making, the physics-based model is replaced by a cheaper surrogate model. A two-step surrogate model is proposed when...

... Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 18 10 2021 16 02 2022 17 02 2022 08 04 2022 conceptual design data-driven design design theory and methodology machine learning uncertainty analysis...

.... (14) RNPC m = ∑ i = 1 132 RNPC t design theory systems design systems engineering uncertainty analysis Following multiple successful applications of the concept, designers from other domains have attempted to apply it more broadly [ 6 , 7 ]. However, recent...

.../040615201 [39] Xiu , D. , 2007 , “ Efficient Collocational Approach for Parametric Uncertainty Analysis ,” Commun. Comput. Phys. , 2 ( 2 ), pp. 293 – 309 . [40] Karhunen , K. , 1946 , “ Uber Lineare Methoden in der Wahrscheinlichkeitsrechnung ,” Ann. Acad. Sci. Fenn. Ser. AI...

...) graphene platelets (GPLs) critical load Gauss–Laguerre quadrature realization vector set design optimization design theory and methodology robust design uncertainty analysis uncertainty modeling Email: jcheng@zju.edu.cn Email: desmend21825118@163.com Email: 13165943998@163.com...

... reliability reliability in design uncertainty analysis Directorate for Engineering 10.13039/100000084 1923799 In engineering design, physics-based reliability is commonly used to predict the probability of failure using physical models derived from physical principles. Such a model...