Release Notes¶
Major Version 3¶
Warning
The major version number was increased from 2 to 3 as changes were made to the input and output formats of quoFEM app. This means that old examples will not be loaded in this version of the tool.
Version 3.2.0 (Current)
Release date: September. 2022
- Highlights
Support for a gradient-free optimization and stochastic Kriging
Fast global sensitivity analysis for very high dimensional output (tested on 2 million QoIs)
New option to discard working directories after each model simulation
Support for PLoM on DesignSafe
Significantly enhanced speed of surrogate validation and prediction
None option for FEM
Improved user interface including error bounds of the surrogate prediction
Major renaming:
OpenseesPy → python
Parameters estimation → deterministic calibration
Inverse problem → Bayesian calibration
- Current Availability
New features and fixes in this release are marked blue in the following list of features.
UQ (Uncertainty Quantification and Optimization Options):
Dakota: [← New option to discard working directories after each model evaluation]
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Deterministic Calibration [← formerly Parameter Estimation]:
NL2SOL
OPT++GaussNewton
Bayesian Calibration [← formerly Inverse Problem]:
DREAM
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
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
Sampling
Monte Carlo Sampling (MCS) a. Resample from an existing correlated dataset of samples
Train Gaussian Process (GP) Surrogate Model [← Enhanced speed and stability]
Multifidelity surrogate modeling
Adaptive design of experiment options for surrogate modeling
Nugget optimization options for surrogate modeling
Stochastic Kriging
Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
Python [← formerly OpenSeesPy]:
Custom
SurrogateGP
None
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Inspect points on the 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
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ
Forward Uncertainty Propagation
Global Sensitivity Analysis (PM-GSA)
Train GP Surrogate Model
PLoM
UCSD_UQ
TMCMC
Version 3.1.0
Release date: June. 2022
New algorithm: Principal component analysis and probabilistic model-based GSA
“NaN” handling improved in the SimCenterUQ engine
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability (terminology and expressions revised)
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
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
Sampling
Monte Carlo Sampling (MCS) a. Resample from an existing correlated dataset of samples
Train Gaussian Process (GP) Surrogate Model
Multifidelity surrogate modeling
Adaptive design of experiment options for surrogate modeling
Nugget optimization options for surrogate modeling
Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
Custom
SurrogateGP
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an 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
Visualization of PLoM training results
PCA representation error plot
Diffusion maps eigenvalue plot
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ
Forward Uncertainty Propagation
PM-GSA
Train GP Surrogate Model
UCSD_UQ
TMCMC
Version 3.0.0
Release date: March. 2022
Major restructuring of the backend
Updated example files
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
Probability Model-based Global Sensitivity Analysis (PM-GSA)
First-order Sobol indices
Group-wise Sobol indices
Sampling
Monte Carlo Sampling (MCS) a. Resample from an existing correlated dataset of samples
Train Gaussian Process (GP) Surrogate Model
Multifidelity surrogate modeling
Adaptive design of experiment options for surrogate modeling
Nugget optimization options for surrogate modeling
Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
- Custom
SurrogateGP
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Inspect points on chart
Visualization of surrogate modeling results
Goodness-of-fit measures
Visualization of PLoM training results
PCA representation error plot
Diffusion maps eigenvalue plot
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ
Forward Uncertainty Propagation
PM-GSA
Train GP Surrogate Model
UCSD_UQ
TMCMC
Version 2.4.1
Release date: Dec. 2021
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
Probability Model-based Global Sensitivity Analysis (PM-GSA)
Sampling
Monte Carlo Sampling (MCS) a. Resample from an existing correlated dataset of samples
Train Gaussian Process (GP) Surrogate Model
Multifidelity surrogate modeling
Adaptive design of experiment options for surrogate modeling
Nugget optimization options for surrogate modeling
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
Custom
SurrogateGP
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Visualization of surrogate modeling results
Goodness-of-fit measures
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Importance Sampling
Sensitivity Analysis:
MCS
LHS
SimCenterUQ
Forward Uncertainty Propagation
PM-GSA
Train GP Surrogate Model
UCSD_UQ
TMCMC
Version 2.4.0
Release date: Oct. 2021
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
Probability Model-based Global Sensitivity Analysis (PM-GSA)
Sampling
Monte Carlo Sampling (MCS)
Resample from an existing correlated dataset of samples
Train Gaussian Process (GP) Surrogate Model
Multifidelity surrogate modeling
Adaptive design of experiment options for surrogate modeling
Nugget optimization options for surrogate modeling
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
Custom
SurrogateGP
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Visualization of surrogate modeling results
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
SimCenterUQ
Forward Uncertainty Propagation
PM-GSA
Train GP Surrogate Model
Version 2.3
Release date: May 2021
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
Probability Model-based Global Sensitivity Analysis (PM-GSA)
Sampling
Monte Carlo Sampling (MCS)
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
Override default log-likelihood function
Override default error covariance structure
Calibrate multipliers on error covariance structure
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
Custom
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
EDP (Outputs from Computational Models):
Scalar quantities of interest
Vector quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
Version 2.2
Release date: Oct. 2020
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
SimCenterUQ:
Sensitivity Analysis
Probability Model-based Global Sensitivity Analysis (PM-GSA)
Sampling
Monte Carlo Sampling (MCS)
UCSD_UQ:
Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation
CustomUQ:
Configure UQ analysis using JSON file
FEM (Computational Model Specification):
OpenSees
FEAPpv
OpenSeesPy
Custom
RV (Inputs to Computational Models):
Inspect PDF of RV
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
SimCenterUQ:
:blue:`Random variables (UQ): RVs can be defined by any of these three options - parameters, moments, or dataset.
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Exponential
Discrete
Gamma
Chi-squared
Truncated exponential
UCSD_UQ:
Random variables (Priors):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
EDP (Outputs from Computational Models):
Scalar quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
Local Reliability
Global Reliability
Sensitivity Analysis:
MCS
LHS
Version 2.0
Release date: Sept. 2019
This is a SimCenter research application whose purpose is to allow users to perform uncertainty quantification and optimization utilizing existing finite element applications.
It will run the computations locally utilizing a laptop/desktop or remotely utilizing the computational resources at TACC made available through DesignSafe-CI.
Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)
UQ (Uncertainty Quantification and Optimization Options):
Dakota:
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Parameter Estimation:
NL2SOL
OPT++GaussNewton
Inverse Problem:
DREAM
Reliability:
FORM
SORM
Sensitivity Analysis:
MCS
LHS
FEM (Computational Model Specification):
OpenSees
FEAPpv
RV (Inputs to Computational Models):
Dakota:
Random variables (UQ):
Normal
Lognormal
Beta
Uniform
Weibull
Gumbel
Design variables (Optimization):
Continuous
EDP (Outputs from Computational Models):
Scalar quantities of interest
RES (Summary and Visualization of UQ Analysis Results):
Summary statistics of outputs displayed
Mean
Standard deviation
All output values presented in the spreadsheet
Update the chart by clicking on spreadsheet columns
Output values visualized in an interactive chart
Scatter plot
Histogram
Cumulative distribution
Remote (Support for Analysis on DesignSafe’s high-performance supercomputer):
Dakota
Forward Uncertainty Propagation:
Monte Carlo Sampling (MCS)
Latin Hypercube Sampling (LHS)
Importance Sampling
Gaussian Process Regression
Polynomial Chaos Expansion
Reliability:
FORM
SORM
Sensitivity Analysis:
MCS
LHS
We encourage new feature suggestions, please write to us at lblBugs.