国际不确定性的量化期刊
每年出版 6 期
ISSN 打印: 2152-5080
ISSN 在线: 2152-5099
IF:
1.7
5-Year IF:
1.9
Immediacy Index:
0.5
Eigenfactor:
0.0007
JCI:
0.5
SJR:
0.584
SNIP:
0.676
CiteScore™::
3
H-Index:
25
Indexed in
卷 10, 2020 册 5
Special Issue: Multilevel-Multifidelity Approaches for Uncertainty Quantification Part 1
Guest Editors: Gianluca Geraci, Gianluca Iaccarino, and Michael Eldred
DOI: 10.1615/Int.J.UncertaintyQuantification.v10.i5
FOREWORD: SPECIAL ISSUE ON MULTILEVEL-MULTIFIDELITY APPROACHES FOR UNCERTAINTY QUANTIFICATION
pp. v-ix
DOI: 10.1615/Int.J.UncertaintyQuantification.2020036984
DATA-CONSISTENT SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING A PROBABILISTIC MULTI-FIDELITY METHOD BASED ON CONDITIONAL DENSITIES
pp. 399-424
DOI: 10.1615/Int.J.UncertaintyQuantification.2020030092
MULTI-FIDELITY MODELING OF PROBABILISTIC AERODYNAMIC DATABASES FOR USE IN AEROSPACE ENGINEERING
pp. 425-447
DOI: 10.1615/Int.J.UncertaintyQuantification.2020032841
MULTIFIDELITY ESTIMATORS FOR CORONARY CIRCULATION MODELS UNDER CLINICALLY INFORMED DATA UNCERTAINTY
pp. 449-466
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033068
VARIANCE REDUCTION METHODS AND MULTILEVEL MONTE CARLO STRATEGY FOR ESTIMATING DENSITIES OF SOLUTIONS TO RANDOM SECOND-ORDER LINEAR DIFFERENTIAL EQUATIONS
pp. 467-497
DOI: 10.1615/Int.J.UncertaintyQuantification.2020032659
最新一期
将发表的论文
EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS
Application of global sensitivity analysis for identification of probabilistic design spaces
Stochastic Galerkin method and port-Hamiltonian form for linear first-order ordinary differential equations
Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models