研飞ivySCI (青藤学术）

International Journal for Uncertainty Quantification

短名	Int. J. UncertaintyQuantification
Journal Impact	1.59
国际分区	MATHEMATICS, INTERDISCIPLINARY APPLICATIONS(Q3)
期刊索引	SCI Q3中科院 4 区

ISSN	2152-5080, 2152-5099
h-index	27
国内分区	工程技术(4区)工程技术工程综合(4区)工程技术数学跨学科应用(4区)

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION 在存在不确定性的情况下传播复杂系统的分析、建模、设计和控制领域的永久兴趣信息。该杂志力求强调跨随机分析、统计建模和科学计算的方法。感兴趣的系统由可能具有多尺度特征的微分方程控制。特别感兴趣的主题包括不确定性的表示、跨尺度的不确定性传播、解决维度灾难、随机偏微分方程的长期积分、构建随机模型的数据驱动方法、预测计算科学的验证、验证和不确定性量化，以及高维空间中不确定性的可视化。贝叶斯计算和机器学习技术也很有趣，例如在随机多尺度系统的背景下，用于模型选择/分类和决策。特别鼓励有关现代实验和建模方法与预测科学的动态耦合的报告。在物理和生物科学的所有领域中应用不确定性量化都是合适的。

期刊主页投稿网址

涉及主题	计算机科学数学统计机器学习物理不确定度量化人工智能工程类数学优化量子力学应用数学算法数学分析
出版信息	出版商: Begell House Inc.，出版周期: ，期刊类型: journal
基本数据	创刊年份: 2014，原创研究文献占比: 100.00%，自引率:6.70%， Gold OA占比: 0.00%

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INDEX

2024-1-1

SENSITIVITY ANALYSES OF A MULTI-PHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS

2024-1-1

HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS

2024-1-1

Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors

2024-1-1

Application of global sensitivity analysis for identification of probabilistic design spaces

2024-1-1

SOLVING STOCHASTIC INVERSE PROBLEMS FOR CFD USING DATA-CONSISTENT INVERSION AND AN ADAPTIVE STOCHASTIC COLLOCATION METHOD

2024-1-1

BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

2024-1-1

A DIMENSION-ADAPTIVE COMBINATION TECHNIQUE FOR UNCERTAINTY QUANTIFICATION

2024-1-1

Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

2024-1-1

UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING

2024-1-1

Extremes of vector-valued processes by finite dimensional models

2024-1-1

LONG SHORT-TERM RELEVANCE LEARNING

2024-1-1

IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH

2024-1-1

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

2024-1-1

A BAYESIAN NEURAL NETWORK APPROACH TO MULTI-FIDELITY SURROGATE MODELING

2024-1-1

Learning a class of stochastic differential equations via numerics-informed Bayesian denoising

2024-1-1

LIKELIHOOD AND DEPTH-BASED CRITERIA FOR COMPARING SIMULATION RESULTS WITH EXPERIMENTAL DATA, IN SUPPORT OF VALIDATION OF NUMERICAL SIMULATORS

2024-1-1

Uncertainty Analysis for Drift-Diffusion Equations

2024-1-1

EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS

2024-1-1

Probabilistic Uncertainty Propagation Using Gaussian Process Surrogates

2024-1-1

A DOMAIN-DECOMPOSED VAE METHOD FOR BAYESIAN INVERSE PROBLEMS

2024-1-1

MORE POWERFUL HSIC-BASED INDEPENDENCE TESTS, EXTENSION TO SPACE-FILLING DESIGNS AND FUNCTIONAL DATA

2024-1-1

Measuring inputs-outputs association for time-depending hazard models under safety objectives using kernels

2024-1-1

HYPER-DIFFERENTIAL SENSITIVITY ANALYSIS FOR NONLINEAR BAYESIAN INVERSE PROBLEMS

2024-1-1

Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models

2024-1-1

A novel probabilistic transfer learning strategy for polynomial regression

2024-1-1

Stochastic Galerkin method and port-Hamiltonian form for linear first-order ordinary differential equations

2024-1-1

Covariance estimation using h-statistics in Monte Carlo and multilevel Monte Carlo methods

2024-1-1

Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

2024-1-1

Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems

2024-1-1

PARALLEL PARTIAL EMULATION IN APPLICATIONS

2024-1-1

Bayesian³ Active learning for regularized arbitrary multi-element polynomial chaos using information theory

2024-1-1

MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY

2024-1-1

Variance-based sensitivity of Bayesian inverse problems to the prior distribution

2024-1-1

NESTED OPTIMAL UNCERTAINTY QUANTIFICATION FOR AN EFFICIENT INCORPORATION OF RANDOM FIELDS-APPLICATION TO SHEET METAL FORMING

2024-1-1

DECISION THEORETIC BOOTSTRAPPING

2024-1-1

Structure-Preserving Model Order Reduction of Random Parametric Linear Systems via Regression

2024-1-1

A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics

2024-1-1

DECISION THEORETIC BOOTSTRAPPING

2023-12-22

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

2023-12-11

A DOMAIN-DECOMPOSED VAE METHOD FOR BAYESIAN INVERSE PROBLEMS

2023-12-6

MORE POWERFUL HSIC-BASED INDEPENDENCE TESTS, EXTENSION TO SPACE-FILLING DESIGNS AND FUNCTIONAL DATA

2023-12-1

MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY

2023-11-29

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International Journal for Uncertainty Quantification

期刊引文格式

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最新文章

INDEX

SENSITIVITY ANALYSES OF A MULTI-PHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS

HYPERDIFFERENTIAL SENSITIVITY ANALYSIS IN THE CONTEXT OF BAYESIAN INFERENCE APPLIED TO ICE-SHEET PROBLEMS

Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors

Application of global sensitivity analysis for identification of probabilistic design spaces

SOLVING STOCHASTIC INVERSE PROBLEMS FOR CFD USING DATA-CONSISTENT INVERSION AND AN ADAPTIVE STOCHASTIC COLLOCATION METHOD

BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

A DIMENSION-ADAPTIVE COMBINATION TECHNIQUE FOR UNCERTAINTY QUANTIFICATION

Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING

Extremes of vector-valued processes by finite dimensional models

LONG SHORT-TERM RELEVANCE LEARNING

IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

A BAYESIAN NEURAL NETWORK APPROACH TO MULTI-FIDELITY SURROGATE MODELING

Learning a class of stochastic differential equations via numerics-informed Bayesian denoising

LIKELIHOOD AND DEPTH-BASED CRITERIA FOR COMPARING SIMULATION RESULTS WITH EXPERIMENTAL DATA, IN SUPPORT OF VALIDATION OF NUMERICAL SIMULATORS

Uncertainty Analysis for Drift-Diffusion Equations

EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS

Probabilistic Uncertainty Propagation Using Gaussian Process Surrogates

A DOMAIN-DECOMPOSED VAE METHOD FOR BAYESIAN INVERSE PROBLEMS

MORE POWERFUL HSIC-BASED INDEPENDENCE TESTS, EXTENSION TO SPACE-FILLING DESIGNS AND FUNCTIONAL DATA

Measuring inputs-outputs association for time-depending hazard models under safety objectives using kernels

HYPER-DIFFERENTIAL SENSITIVITY ANALYSIS FOR NONLINEAR BAYESIAN INVERSE PROBLEMS

Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models

A novel probabilistic transfer learning strategy for polynomial regression

Stochastic Galerkin method and port-Hamiltonian form for linear first-order ordinary differential equations

Covariance estimation using h-statistics in Monte Carlo and multilevel Monte Carlo methods

Bayesian Parameter Inference for Partially Observed Diffusions using Multilevel Stochastic Runge-Kutta Methods

Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems

PARALLEL PARTIAL EMULATION IN APPLICATIONS

Bayesian³ Active learning for regularized arbitrary multi-element polynomial chaos using information theory

MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY

Variance-based sensitivity of Bayesian inverse problems to the prior distribution

NESTED OPTIMAL UNCERTAINTY QUANTIFICATION FOR AN EFFICIENT INCORPORATION OF RANDOM FIELDS-APPLICATION TO SHEET METAL FORMING

DECISION THEORETIC BOOTSTRAPPING

Structure-Preserving Model Order Reduction of Random Parametric Linear Systems via Regression

A Bayesian Calibration Framework with Embedded Model Error for Model Diagnostics

DECISION THEORETIC BOOTSTRAPPING

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

A DOMAIN-DECOMPOSED VAE METHOD FOR BAYESIAN INVERSE PROBLEMS

MORE POWERFUL HSIC-BASED INDEPENDENCE TESTS, EXTENSION TO SPACE-FILLING DESIGNS AND FUNCTIONAL DATA

MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY

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