Boosting efficiency and reducing graph reliance: Basis adaptation integration in Bayesian multi-fidelity networks
The computational cost of high-fidelity numerical models makes outer-loop analysis, which
requires repeated interrogation of the model such as uncertainty quantification …
requires repeated interrogation of the model such as uncertainty quantification …
GP+: a python library for kernel-based learning via Gaussian Processes
In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …
Gaussian processes (GPs) which are powerful statistical models that are completely …
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate …
JD Jakeman - Environmental Modelling & Software, 2023 - Elsevier
PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models
such as those used in the earth, environmental and engineering sciences. Easy to use and …
such as those used in the earth, environmental and engineering sciences. Easy to use and …
Multifidelity uncertainty quantification with models based on dissimilar parameters
Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to
significantly reduce the variance of statistical estimators while preserving the bias of the …
significantly reduce the variance of statistical estimators while preserving the bias of the …
Multi-output multilevel best linear unbiased estimators via semidefinite programming
Multifidelity forward uncertainty quantification (UQ) problems often involve multiple
quantities of interest and heterogeneous models (eg, different grids, equations, dimensions …
quantities of interest and heterogeneous models (eg, different grids, equations, dimensions …
Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques
We study the problem of multifidelity uncertainty propagation for computationally expensive
models. In particular, we consider the general setting where the high-fidelity and low-fidelity …
models. In particular, we consider the general setting where the high-fidelity and low-fidelity …
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Abstract Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for
variance reduction to make tractable uncertainty quantification even when numerically …
variance reduction to make tractable uncertainty quantification even when numerically …
Multi-fidelity uncertainty quantification for homogenization problems in structure-property relationships from crystal plasticity finite elements
Crystal plasticity finite element method (CPFEM) has been an integrated computational
materials engineering (ICME) workhorse to study materials behaviors and structure-property …
materials engineering (ICME) workhorse to study materials behaviors and structure-property …
Multifidelity linear regression for scientific machine learning from scarce data
Machine learning (ML) methods, which fit to data the parameters of a given parameterized
model class, have garnered significant interest as potential methods for learning surrogate …
model class, have garnered significant interest as potential methods for learning surrogate …
On the optimization of approximate control variates with parametrically defined estimators
Abstract Multi-model Monte Carlo methods, such as multi-level Monte Carlo (MLMC) and
multifidelity Monte Carlo (MFMC), allow for efficient estimation of the expectation of a …
multifidelity Monte Carlo (MFMC), allow for efficient estimation of the expectation of a …