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 4
DOI: 10.1615/Int.J.UncertaintyQuantification.v10.i4
SENSITIVITY INDICES FOR OUTPUT ON A RIEMANNIAN MANIFOLD
pp. 297-314
DOI: 10.1615/Int.J.UncertaintyQuantification.2020029614
A MULTI-FIDELITY NEURAL NETWORK SURROGATE SAMPLING METHOD FOR UNCERTAINTY QUANTIFICATION
pp. 315-332
DOI: 10.1615/Int.J.UncertaintyQuantification.2020031957
INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL
pp. 333-349
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033186
YIELD OPTIMIZATION BASED ON ADAPTIVE NEWTON-MONTE CARLO AND POLYNOMIAL SURROGATES
pp. 351-373
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033344
DATA-DRIVEN CALIBRATION OF P3D HYDRAULIC FRACTURING MODELS
pp. 375-398
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033602
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