Release Notes

Major Version 3

Warning

The major version number was increased from 2 to 3 as changes were made to input and output formats of quoFEM app. This means old examples will not be loaded in this version of the tool.

Version 3.2.0 (Current)

Release date: September. 2022

Highlights

  1. Support for a gradient-free optimization and stochastic Kriging

  2. Fast global sensitivity analysis for very high dimensional output (tested on 2 million QoIs)

  3. New option to discard working directories after each model simulation

  4. Support for PLoM on DesignSafe

  5. Significantly enhanced speed of surrogate validation and prediction

  6. None option for FEM

  7. Improved user interface including error bounds of the surrogate prediction

  8. 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.

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota: [← New option to discard working directories after each model evaluation]

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Deterministic Calibration [← formerly Parameter Estimation]:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Bayesian Calibration [← formerly Inverse Problem]:

        1. DREAM

      4. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

          1. First-order Sobol indices

          2. Total-effect Sobol indices

          3. Group-wise Sobol indices

          4. Principal component analysis and probabilistic model-based GSA (PCA-PSA) for high-dimensional QoIs

          5. Aggregated Sobol indices for field QoIs

          6. Import input/output samples from data files

      2. Sampling

        1. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples

      3. Train Gaussian Process (GP) Surrogate Model [← Enhanced speed and stability]

        1. Multifidelity surrogate modeling

        2. Adaptive design of experiments options for surrogate modeling

        3. Nugget optimization options for surrogate modeling

        4. Stochastic Kriging

      4. Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

        1. Override default log-likelihood function

        2. Override default error covariance structure

        3. Calibrate multipliers on error covariance structure

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • Python [← formerly OpenSeesPy]:

    • Custom

    • SurrogateGP

    • None

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

      4. Inspect points on chart

    • Spreadsheet save options

      1. Save Table

      2. Save Columns Separately (Useful after Bayesian updating, the posterior samples can later be directly loaded in quoFEM)

      3. Save RVs (Useful for surrogate model training)

      4. Save QoIs (Useful for surrogate model training)

      5. Save Surrogate Predictions (Only for the surrogate model results)

    • Visualization of surrogate modeling results

      1. Goodness-of-fit measures

      2. 90% confidence interval and prediction interval

    • Visualization of PLoM training results

      1. PCA representation error plot

      2. Diffusion maps eigenvalue plot

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ

      1. Forward Uncertainty Propagation

      2. Global Sensitivity Analysis (PM-GSA)

      3. Train GP Surrogate Model

      4. PLoM

    • UCSD_UQ

      1. TMCMC

Version 3.1.0

Release date: June. 2022

  1. New algorithm: Principal component analysis and probabilistic model-based GSA

  2. “NaN” handling improved in SimCenterUQ engine

Current Availability (New features and fixes in this release are denoted with a blue font color in the following list of features.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. Local Reliability (terminology and expressions revised)

        2. Global Reliability

        3. Importance Sampling

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

          1. First-order Sobol indices

          2. Total-effect Sobol indices

          3. Group-wise Sobol indices

          4. Principal component analysis and probabilistic model-based GSA (PCA-PSA) for high-dimensional QoIs

          5. Aggregated Sobol indices for field QoIs

      2. Sampling

        1. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples

      3. Train Gaussian Process (GP) Surrogate Model

        1. Multifidelity surrogate modeling

        2. Adaptive design of experiments options for surrogate modeling

        3. Nugget optimization options for surrogate modeling

      4. Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

        1. Override default log-likelihood function

        2. Override default error covariance structure

        3. Calibrate multipliers on error covariance structure

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

    • SurrogateGP

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

      4. Inspect points on chart

    • Spreadsheet save options

      1. Save Table

      2. Save Columns Separately (Useful after Bayesian updating, the posterior samples can later be directly loaded in quoFEM)

      3. Save RVs (Useful for surrogate model training)

      4. Save QoIs (Useful for surrogate model training)

      5. Save Surrogate Predictions (Only for the surrogate model results)

    • Visualization of surrogate modeling results

      1. Goodness-of-fit measures

    • Visualization of PLoM training results

      1. PCA representation error plot

      2. Diffusion maps eigenvalue plot

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ

      1. Forward Uncertainty Propagation

      2. PM-GSA

      3. Train GP Surrogate Model

    • UCSD_UQ

      1. TMCMC

Version 3.0.0

Release date: March. 2022

  1. Major restructuring of the backend

  2. 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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

    1. Parameter Estimation:

      1. NL2SOL

      2. OPT++GaussNewton

    2. Inverse Problem:

      1. DREAM

    3. Reliability:

      1. Local Reliability

      2. Global Reliability

      3. Importance Sampling

    4. Sensitivity Analysis:

      1. MCS

      2. LHS

  • SimCenterUQ:

    1. Sensitivity Analysis

      1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

        1. First-order Sobol indices

        2. Group-wise Sobol indices

    2. Sampling

      1. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples

    3. Train Gaussian Process (GP) Surrogate Model

      1. Multifidelity surrogate modeling

      2. Adaptive design of experiments options for surrogate modeling

      3. Nugget optimization options for surrogate modeling

    4. Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)

  • UCSD_UQ:

    1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

      1. Override default log-likelihood function

      2. Override default error covariance structure

      3. Calibrate multipliers on error covariance structure

  • CustomUQ:

    1. Configure UQ analysis using JSON file

  1. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

    • SurrogateGP

  2. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

  3. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  4. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

      4. Inspect points on chart

    • Visualization of surrogate modeling results

      1. Goodness-of-fit measures

    • Visualization of PLoM training results

      1. PCA representation error plot

      2. Diffusion maps eigenvalue plot

  5. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ

      1. Forward Uncertainty Propagation

      2. PM-GSA

      3. Train GP Surrogate Model

    • UCSD_UQ

      1. TMCMC

Major Version 2

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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

      2. Sampling

        1. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples

      3. Train Gaussian Process (GP) Surrogate Model

        1. Multifidelity surrogate modeling

        2. Adaptive design of experiments options for surrogate modeling

        3. Nugget optimization options for surrogate modeling

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

        1. Override default log-likelihood function

        2. Override default error covariance structure

        3. Calibrate multipliers on error covariance structure

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

    • SurrogateGP

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

    • Visualization of surrogate modeling results

      1. Goodness-of-fit measures

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Gaussian Process Regression

        4. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

        3. Importance Sampling

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ

      1. Forward Uncertainty Propagation

      2. PM-GSA

      3. Train GP Surrogate Model

    • UCSD_UQ

      1. 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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. Local Reliability

        2. Global Reliability

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

      2. Sampling

        1. Monte Carlo Sampling (MCS)

          1. Resample from existing correlated dataset of samples

      3. Train Gaussian Process (GP) Surrogate Model

        1. Multifidelity surrogate modeling

        2. Adaptive design of experiments options for surrogate modeling

        3. Nugget optimization options for surrogate modeling

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

        1. Override default log-likelihood function

        2. Override default error covariance structure

        3. Calibrate multipliers on error covariance structure

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

    • SurrogateGP

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

    • Visualization of surrogate modeling results

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ

      1. Forward Uncertainty Propagation

      2. PM-GSA

      3. 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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. Local Reliability

        2. Global Reliability

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

      2. Sampling

        1. Monte Carlo Sampling (MCS)

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

        1. Override default log-likelihood function

        2. Override default error covariance structure

        3. Calibrate multipliers on error covariance structure

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

    • Vector quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

      3. Sensitivity Analysis:

        1. MCS

        2. 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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. Local Reliability

        2. Global Reliability

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

    • SimCenterUQ:

      1. Sensitivity Analysis

        1. Probability Model-based Global Sensitivity Analysis (PM-GSA)

      2. Sampling

        1. Monte Carlo Sampling (MCS)

    • UCSD_UQ:

      1. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation

    • CustomUQ:

      1. Configure UQ analysis using JSON file

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

    • OpenSeesPy

    • Custom

  3. RV (Inputs to Computational Models):

    • Inspect PDF of RV

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

    • SimCenterUQ:

      1. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

        7. Exponential

        8. Discrete

        9. Gamma

        10. Chi-squared

        11. Truncated exponential

    • UCSD_UQ:

      1. Random variables (Priors):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Reliability:

        1. Local Reliability

        2. Global Reliability

      3. Sensitivity Analysis:

        1. MCS

        2. 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 applictions.

It will run the computations locally utilizing 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.)

  1. UQ (Uncertainty Quantification and Optimization Options):

    • Dakota:

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Parameter Estimation:

        1. NL2SOL

        2. OPT++GaussNewton

      3. Inverse Problem:

        1. DREAM

      4. Reliability:

        1. FORM

        2. SORM

      5. Sensitivity Analysis:

        1. MCS

        2. LHS

  2. FEM (Computational Model Specification):

    • OpenSees

    • FEAPpv

  3. RV (Inputs to Computational Models):

    • Dakota:

      1. Random variables (UQ):

        1. Normal

        2. Lognormal

        3. Beta

        4. Uniform

        5. Weibull

        6. Gumbel

      2. Design variables (Optimization):

        1. Continuous

  4. EDP (Outputs from Computational Models):

    • Scalar quantities of interest

  5. RES (Summary and Visualization of UQ Analysis Results):

    • Summary statistics of outputs displayed

      1. Mean

      2. Standard deviation

    • All output values presented in spreadsheet

      1. Update chart by clicking on spreadsheet columns

    • Output values visualized in interactive chart

      1. Scatter plot

      2. Histogram

      3. Cumulative distribution

  6. Remote (Support for Analysis on DesignSafe’s high performance super computer):

    • Dakota

      1. Forward Uncertainty Propagation:

        1. Monte Carlo Sampling (MCS)

        2. Latin Hypercube Sampling (LHS)

        3. Importance Sampling

        4. Gaussian Process Regression

        5. Polynomial Chaos Expansion

      2. Reliability:

        1. FORM

        2. SORM

      3. Sensitivity Analysis:

        1. MCS

        2. LHS

We encourage new feature suggestions, please write to us at Bugs & Feature Requests.