5. Capabilities¶
Version 3.2 of the quoFEM app was released in September 22. The following lists the functionality available in this current version. (Note: New features and fixes in this release are marked blue in the following list of features.)
5.1. UQ (Uncertainty Quantification and Optimization Options)¶
Forward Uncertainty Propagation
Dakota [← New option to discard working directories after each model evaluation]
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
-
Monte Carlo Sampling (MCS)
Resample from existing correlated dataset of samples
Global Sensitivity Analysis
-
MCS
LHS
-
Probability Model-based Global Sensitivity Analysis (PM-GSA)
First-order Sobol indices
Total-effect Sobol indices
Group-wise Sobol indices
Principal component analysis and probabilistic model-based GSA (PCA-PSA) for high-dimensional QoIs
Aggregated Sobol indices for field QoIs
Import input/output samples from data files
-
Reliability Analysis
-
Local Reliability
Global Reliability
Importance Sampling
-
Bayesian Calibration [← formerly Inverse Problem]
Deterministic Calibration [← formerly Parameter Estimation]
-
NL2SOL
OPT++GaussNewton
Gradient-free optimization
-
Surrogate Modeling
SimCenterUQ
Train Gaussian Process (GP) Surrogate Model [← Enhanced speed and stability]
Multifidelity surrogate modeling
Adaptive design of experiments options for surrogate modeling
Nugget optimization options for surrogate modeling
Stochastic Kriging
Surrogate modeling using Probabilistic Learning on Manifolds (PLoM) *
-
Configure UQ analysis using JSON file
Note
Support for the running computation to be preformed on TACC’s high performance computer, Frontera, is provided through DesignSafe for all but the methods indicated with a star (*).
5.2. FEM (Computational Model Specification)¶
OpenSees
FEAPpv
Python [← formerly OpenSeesPy]
Custom
SurrogateGP
None
5.3. RV (Random Variable Options)¶
Inspect PDF of RV
Distributions available: 1
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Continuous 2
Exponential 3
Discrete 3
Gamma 3
Chi-squared 3
Truncated exponential 3
Note
1: For SimCentreUQ and UCSD algorithms only, the RVs can be defined by any of parameters, moments, or a dataset. 2: Available for Optimization routines in Dakota only. 3: Avaliable in SimCenterUQ and UCSD only.
5.4. EDP (Outputs from Computational Models)¶
Scalar quantities of interest
Vector quantities of interest
5.5. RES (Summary and Visualization of UQ Analysis Results)¶
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in spreadsheet
Update chart by clicking on spreadsheet columns
Output values visualized in interactive chart
Scatter plot
Histogram
Cumulative distribution
Inspect points on chart
Spreadsheet save options
Save Table
Save Columns Separately (Useful after Bayesian updating, the posterior samples can later be directly loaded in quoFEM)
Save RVs (Useful for surrogate model training)
Save QoIs (Useful for surrogate model training)
Save Surrogate Predictions (Only for the surrogate model results)
Visualization of surrogate modeling results
Goodness-of-fit measures
90% confidence interval and prediction interval
Visualization of PLoM training results
PCA representation error plot
Diffusion maps eigenvalue plot