Computational toolkits for model-based design and optimization
Highlights•Systematically review 82 model-based design (MBD) toolkits.•Dynamic,
multiscale, and interdisciplinary grand challenges drive software trends.•Organize MBD …
multiscale, and interdisciplinary grand challenges drive software trends.•Organize MBD …
Algorithm 1040: The Sparse Grids Matlab Kit-a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be
used for approximating high-dimensional functions and, in particular, for surrogate-model …
used for approximating high-dimensional functions and, in particular, for surrogate-model …
[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 …
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 …
[HTML][HTML] Democratizing uncertainty quantification
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the
widespread adoption of sophisticated UQ methods is limited by technical complexity. In this …
widespread adoption of sophisticated UQ methods is limited by technical complexity. In this …
CUQIpy: I. Computational uncertainty quantification for inverse problems in Python
This paper introduces CUQIpy, a versatile open-source Python package for computational
uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series …
uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series …
The Sparse Grids Matlab kit--a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be
used for approximating high-dimensional functions and, in particular, for surrogate-model …
used for approximating high-dimensional functions and, in particular, for surrogate-model …
[PDF][PDF] UM-Bridge: Uncertainty quantification and modeling bridge
L Seelinger, V Cheng-Seelinger… - Journal of Open …, 2023 - durham-repository.worktribe.com
Purpose: We proposed that zinc (Zn) deposition in deciduous teeth would be a timed record
of exposure to this essential micronutrient over very early life. We tested this hypothesis by …
of exposure to this essential micronutrient over very early life. We tested this hypothesis by …
Optimized bayesian framework for inverse heat transfer problems using reduced order methods
A stochastic inverse heat transfer problem is formulated to infer the transient heat flux,
treated as an unknown Neumann boundary condition. Therefore, an Ensemble-based …
treated as an unknown Neumann boundary condition. Therefore, an Ensemble-based …
Modeling and quantifying parameter uncertainty of co‐seismic non‐classical nonlinearity in rocks
Dynamic perturbations reveal unconventional nonlinear behavior in rocks, as evidenced by
field and laboratory studies. During the passage of seismic waves, rocks exhibit a decrease …
field and laboratory studies. During the passage of seismic waves, rocks exhibit a decrease …