4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering最新文献_第2页

EFFICIENT DISCRIMINATION BETWEEN BIOLOGICAL POPULATIONS VIA NEURAL-BASED ESTIMATION OF RÉNYI DIVERGENCE 通过基于神经的rÉnyi差异估计的生物种群之间的有效区分

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8026.19067

A. Tsourtis, G. Papoutsoglou, Yannis Pantazis

引用次数: 0

MACHINE LEARNING AIDED STOCHASTIC SLOPE STABILITY ANALYSIS 机器学习辅助随机边坡稳定性分析

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8023.19068

Zhanpeng Liu, Di Wu, D. Sheng, B. Fatahi, H. Khabbaz

引用次数: 0

OPTIMAL SENSOR PLACEMENT IN DISTRICT HEATING NETWORKS FOR BAYESIAN INFERENCE OF UNCERTAIN DEMANDS 基于不确定需求贝叶斯推理的区域供热网络传感器优化配置

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8031.19135

Alexander Matei, A. Bott, Lea Rehlich, Florian Steinke, S. Ulbrich

引用次数: 1

ON THE INVESTIGATION OF THE EFFECT OF POPULATION UNCERTAINTY ON OPTIMAL SENSOR LOCATIONS 人口不确定性对传感器最优位置影响的研究

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8030.19059

F. Igea, M. Chatzis, A. Cicirello

引用次数: 0

NUMERICAL SIMULATION FOR 3D PRINTED WALL STRUCTURE DURING THE PROCESS OF PRINTING CONSIDERING UNCERTAINTY 考虑不确定性的3d打印墙体结构的数值模拟

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8025.18985

Meron Mengesha, A. Schmidt, Luise Göbel, T. Lahmer, C. Könke

引用次数: 0

AN EXPERIMENTAL STUDY OF VARIABILITY IN DAMPING, FREQUENCY RESPONSE AND MODAL DATA 阻尼、频率响应和模态数据变异性的实验研究

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8039.18992

A. K. Panda, S. Modak

引用次数: 0

IMPROVING THE RATE OF CONVERGENCE OF THE QUASI-MONTE CARLO METHOD IN ESTIMATING EXPECTATIONS ON A GEOTECHNICAL SLOPE STABILITY PROBLEM 提高拟蒙特卡罗方法在岩土边坡稳定问题期望估计中的收敛速度

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8032.18886

P. Blondeel, Pieterjan Robbe, Dirk Nuyens, G. Lombaert, S. Vandewalle

{"title":"IMPROVING THE RATE OF CONVERGENCE OF THE QUASI-MONTE CARLO METHOD IN ESTIMATING EXPECTATIONS ON A GEOTECHNICAL SLOPE STABILITY PROBLEM","authors":"P. Blondeel, Pieterjan Robbe, Dirk Nuyens, G. Lombaert, S. Vandewalle","doi":"10.7712/120221.8032.18886","DOIUrl":"https://doi.org/10.7712/120221.8032.18886","url":null,"abstract":". The propagation of parameter uncertainty through engineering models is a key task in uncertainty quantiﬁcation. In many cases, taking into account this uncertainty involves the estimation of expected values by means of the Monte Carlo method. While the performance of the classical Monte Carlo method is independent of the number of uncertainties, its main drawback is the slow convergence rate of the root mean square error, i.e., O ( N − 1 / 2 ) where N is the number of model evaluations. Under appropriate conditions, the quasi-Monte Carlo method improves the order of convergence to O ( N − 1 ) by using deterministic sample points instead of random sample points. Two examples of such point sets are rank-1 lattice sequences and Sobol’ sequences. However, it is possible to further improve the order of convergence by applying the so-called “tent transformation” to a rank-1 lattice sequence, and by “interlacing” a Sobol’ sequence. In this work, we benchmark these two techniques on a slope stability problem from geotechnical engineering, where the uncertainty is located in the cohesion of the soil. The soil cohesion is modeled as a lognormal random ﬁeld of which realizations are computed by means of the Karhunen–Lo`eve (KL) expansion. The quasi-Monte Carlo points are mapped to the normal distribution required in the KL expansion using a novel truncation strategy. We observe an order of convergence of O ( N − 1 . 5 ) in our numerical experiments.","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134534662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UNCERTAINTY QUANTIFICATION IN THE CLOUD WITH UQCLOUD 用uqcloud对云中不确定度进行量化

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.3929/ETHZ-B-000495417

C. Lataniotis, S. Marelli, B. Sudret

引用次数: 2

ANALYTICAL MODEL FOR FRACTURE IN RANDOM QUASIBRITTLE MEDIA BASED ON EXTREMES OF THE AVERAGING PROCESS 基于平均过程极值的随机准脆性介质断裂解析模型

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8035.18924

M. Vořechovský

{"title":"ANALYTICAL MODEL FOR FRACTURE IN RANDOM QUASIBRITTLE MEDIA BASED ON EXTREMES OF THE AVERAGING PROCESS","authors":"M. Vořechovský","doi":"10.7712/120221.8035.18924","DOIUrl":"https://doi.org/10.7712/120221.8035.18924","url":null,"abstract":". The paper presents an analytical model for prediction of the peak force in concrete specimens loaded in bending (both notched and unnotched). The model is capable of predicting peak force statistics by computing the extreme values of sliding averages of random strength ﬁelds. The local strength of the specimen is modeled by a stationary isotropic random ﬁeld with Gaussian distribution and a given autocorrelation function. The averaging operation represents the progressive loss in material integrity and the associated stress redistribution that takes place prior to reaching the peak load. Once the (linear) averaging process is performed analytically, the resulting random ﬁeld of averaged strength is assumed to represent a series of representative volume elements (RVEs) and the global strength is found by solving for the minimum of such an effective strength ﬁeld. All these operations can be written analytically and there are only four model parameters: the three dimensions of the averaging volume (RVE) and the length of the ﬁnal weakest-link chain. The model is veriﬁed using detailed numerical computations of notched and unnotched concrete beams simulated by mesoscale discrete simulations of concrete fracture performed with probabilistic distributions of model parameters. The numerical model used for veriﬁcation represents material randomness both by assigning random locations to the largest aggregates and by simulating random ﬂuctuations of material parameters via a homogeneous random ﬁeld.","PeriodicalId":444608,"journal":{"name":"4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131632582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NONLINEAR GAUSSIAN PROCESS LATENT FORCE MODELS FOR INPUT ESTIMATION IN HYSTERETIC SYSTEMS 迟滞系统输入估计的非线性高斯过程潜力模型

4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering Pub Date : 1900-01-01 DOI: 10.7712/120221.8017.18937

T. Rogers, Joe D. Longbottom, K. Worden, E. Cross

引用次数: 0