[HTML][HTML] Uqpy v4. 1: Uncertainty quantification with python
This paper presents the latest improvements introduced in Version 4 of the UQpy,
Uncertainty Quantification with Python, library. In the latest version, the code was …
Uncertainty Quantification with Python, library. In the latest version, the code was …
A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling
Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and
design optimization by incorporating data from both high-and various low-fidelity (LF) …
design optimization by incorporating data from both high-and various low-fidelity (LF) …
A bi-fidelity surrogate model for extreme loads on offshore structures
PTT Nguyen, L Manuel - Ocean Engineering, 2024 - Elsevier
Built infrastructure and energy generation systems in the ocean such as platforms,
aquaculture net cages, and wave energy converters (WECs) are designed to sustain …
aquaculture net cages, and wave energy converters (WECs) are designed to sustain …
Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain
C Qian, T Ganter, J Michalenko… - … Analysis and Data …, 2024 - Wiley Online Library
Recently, a surge of interest has been given to quantifying epistemic uncertainty (EU), the
reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the …
reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the …
Covariance-free Multifidelity Control Variates Importance Sampling for Reliability Analysis of Rare Events
Multifidelity modeling has been steadily gaining attention as a tool to address the problem of
exorbitant model evaluation costs that makes the estimation of failure probabilities a …
exorbitant model evaluation costs that makes the estimation of failure probabilities a …
Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities
The\ac {doe}'s\ac {neams} program aims to develop predictive capabilities by applying
computational methods to the analysis and design of advanced reactor and fuel-cycle …
computational methods to the analysis and design of advanced reactor and fuel-cycle …
Multi-fidelity Optimization Methods with Applications to Automotive Crashworthiness and Deep Drawing
A Kaps - 2024 - mediatum.ub.tum.de
The necessity to run nonlinear Finite Element analyses in each iteration of optimization
algorithms can often lead to prohibitive computational costs in structural mechanics …
algorithms can often lead to prohibitive computational costs in structural mechanics …
[HTML][HTML] Probabilistic Performance-Pattern Decomposition (PPPD): Analysis framework and applications to stochastic mechanical systems
Numerous research efforts have been devoted to developing quantitative solutions to
stochastic mechanical systems. In general, the problem is perceived as “solved” when a …
stochastic mechanical systems. In general, the problem is perceived as “solved” when a …