.. _lbl-release_quoFEM: .. role:: blue ************* 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 |app|. This means old examples will not be loaded in this version of the tool. .. dropdown:: Version 3.2.0 (:blue:`Current`) :open: **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:`blue` in the following list of features. #. **UQ (Uncertainty Quantification and Optimization Options)**: * Dakota: :blue:`[← New option to discard working directories after each model evaluation]` a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Deterministic Calibration :blue:`[← formerly Parameter Estimation]`: #. NL2SOL #. OPT++GaussNewton c. Bayesian Calibration :blue:`[← formerly Inverse Problem]`: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) a. First-order Sobol indices b. Total-effect Sobol indices c. Group-wise Sobol indices d. Principal component analysis and probabilistic model-based GSA (PCA-PSA) for high-dimensional QoIs e. Aggregated Sobol indices for field QoIs f. :blue:`Import input/output samples from data files` b. Sampling #. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples c. Train Gaussian Process (GP) Surrogate Model :blue:`[← Enhanced speed and stability]` #. Multifidelity surrogate modeling #. Adaptive design of experiments options for surrogate modeling #. Nugget optimization options for surrogate modeling #. :blue:`Stochastic Kriging` d. Surrogate modeling using Probabilistic Learning on Manifolds (PLoM) * UCSD_UQ: a. 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: a. Configure UQ analysis using JSON file #. **FEM (Computational Model Specification)**: * OpenSees * FEAPpv * Python :blue:`[← formerly OpenSeesPy]`: * Custom * SurrogateGP * :blue:`None` #. **RV (Inputs to Computational Models)**: * Inspect PDF of RV * Dakota: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. 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 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 :blue:`prediction interval` * Visualization of PLoM training results #. PCA representation error plot #. Diffusion maps eigenvalue plot #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling c. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ a. Forward Uncertainty Propagation b. Global Sensitivity Analysis (PM-GSA) c. Train GP Surrogate Model d. :blue:`PLoM` * UCSD_UQ a. TMCMC .. dropdown:: Version 3.1.0 **Release date:** June. 2022 #. New algorithm: Principal component analysis and probabilistic model-based GSA #. "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.) #. **UQ (Uncertainty Quantification and Optimization Options)**: * Dakota: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. :blue:`Local Reliability (terminology and expressions revised)` #. Global Reliability #. Importance Sampling e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) a. First-order Sobol indices b. Total-effect Sobol indices c. Group-wise Sobol indices d. :blue:`Principal component analysis and probabilistic model-based GSA (PCA-PSA) for high-dimensional QoIs` e. :blue:`Aggregated Sobol indices for field QoIs` b. Sampling #. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples c. Train Gaussian Process (GP) Surrogate Model #. Multifidelity surrogate modeling #. Adaptive design of experiments options for surrogate modeling #. Nugget optimization options for surrogate modeling d. Surrogate modeling using Probabilistic Learning on Manifolds (PLoM) * UCSD_UQ: a. 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: a. 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: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. 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 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) #. :blue:`Save RVs` (Useful for surrogate model training) #. :blue:`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 super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling c. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ a. Forward Uncertainty Propagation b. PM-GSA c. Train GP Surrogate Model * UCSD_UQ a. TMCMC .. dropdown:: 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: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) a. First-order Sobol indices b. Group-wise Sobol indices b. Sampling #. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples c. Train Gaussian Process (GP) Surrogate Model #. Multifidelity surrogate modeling #. Adaptive design of experiments options for surrogate modeling #. Nugget optimization options for surrogate modeling d. :blue:`Surrogate modeling using Probabilistic Learning on Manifolds (PLoM)` * UCSD_UQ: a. 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: a. 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: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. Random variables (Priors): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. :blue:`Exponential` #. :blue:`Discrete` #. :blue:`Gamma` #. :blue:`Chi-squared` #. :blue:`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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in interactive chart #. Scatter plot #. Histogram #. Cumulative distribution #. :blue:`Inspect points on chart` * Visualization of surrogate modeling results #. Goodness-of-fit measures * :blue:`Visualization of PLoM training results` #. :blue:`PCA representation error plot` #. :blue:`Diffusion maps eigenvalue plot` #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling c. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ a. Forward Uncertainty Propagation b. PM-GSA c. Train GP Surrogate Model * UCSD_UQ a. TMCMC Major Version 2 ================= .. dropdown:: 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: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) b. Sampling #. Monte Carlo Sampling (MCS) a. Resample from existing correlated dataset of samples c. Train Gaussian Process (GP) Surrogate Model #. Multifidelity surrogate modeling #. Adaptive design of experiments options for surrogate modeling #. Nugget optimization options for surrogate modeling * UCSD_UQ: a. 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: a. 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: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. 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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in 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 super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability #. Importance Sampling c. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ a. Forward Uncertainty Propagation b. PM-GSA c. Train GP Surrogate Model * :blue:`UCSD_UQ` a. :blue:`TMCMC` .. dropdown:: 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: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) b. Sampling #. Monte Carlo Sampling (MCS) a. :blue:`Resample from existing correlated dataset of samples` c. :blue:`Train Gaussian Process (GP) Surrogate Model` #. :blue:`Multifidelity surrogate modeling` #. :blue:`Adaptive design of experiments options for surrogate modeling` #. :blue:`Nugget optimization options for surrogate modeling` * UCSD_UQ: a. 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: a. Configure UQ analysis using JSON file #. **FEM (Computational Model Specification)**: * OpenSees * FEAPpv * OpenSeesPy * Custom * :blue:`SurrogateGP` #. **RV (Inputs to Computational Models)**: * Inspect PDF of RV * Dakota: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. 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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in interactive chart #. Scatter plot #. Histogram #. Cumulative distribution * :blue:`Visualization of surrogate modeling results` #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability c. Sensitivity Analysis: #. MCS #. LHS * :blue:`SimCenterUQ` a. :blue:`Forward Uncertainty Propagation` b. :blue:`PM-GSA` c. :blue:`Train GP Surrogate Model` .. dropdown:: 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: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability e. Sensitivity Analysis: #. MCS #. LHS * SimCenterUQ: a. Sensitivity Analysis #. Probability Model-based Global Sensitivity Analysis (PM-GSA) b. Sampling #. Monte Carlo Sampling (MCS) * UCSD_UQ: a. Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation #. :blue:`Override default log-likelihood function` #. :blue:`Override default error covariance structure` #. :blue:`Calibrate multipliers on error covariance structure` * CustomUQ: a. Configure UQ analysis using JSON file #. **FEM (Computational Model Specification)**: * OpenSees * FEAPpv * OpenSeesPy * Custom #. **RV (Inputs to Computational Models)**: * Inspect PDF of RV * Dakota: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * SimCenterUQ: a. Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset. #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. Exponential #. Discrete #. Gamma #. Chi-squared #. Truncated exponential * UCSD_UQ: a. Random variables (Priors): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel #. **EDP (Outputs from Computational Models)**: * Scalar quantities of interest * :blue:`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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in interactive chart #. Scatter plot #. Histogram #. Cumulative distribution #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability c. Sensitivity Analysis: #. MCS #. LHS .. dropdown:: 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: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. Reliability: #. Local Reliability #. Global Reliability e. Sensitivity Analysis: #. MCS #. LHS * :blue:`SimCenterUQ`: a. :blue:`Sensitivity Analysis` #. :blue:`Probability Model-based Global Sensitivity Analysis (PM-GSA)` b. :blue:`Sampling` #. :blue:`Monte Carlo Sampling (MCS)` * :blue:`UCSD_UQ`: a. :blue:`Transitional Markov Chain Monte Carlo (TMCMC) for Bayesian estimation` * :blue:`CustomUQ`: a. :blue:`Configure UQ analysis using JSON file` #. **FEM (Computational Model Specification)**: * OpenSees * FEAPpv * :blue:`OpenSeesPy` * :blue:`Custom` #. **RV (Inputs to Computational Models)**: * :blue:`Inspect PDF of RV` * Dakota: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. Design variables (Optimization): #. Continuous * :blue:`SimCenterUQ`: a. :blue:`Random variables (UQ): RVs can be defined by any of parameters, moments, or dataset.` #. :blue:`Normal` #. :blue:`Lognormal` #. :blue:`Beta` #. :blue:`Uniform` #. :blue:`Weibull` #. :blue:`Gumbel` #. :blue:`Exponential` #. :blue:`Discrete` #. :blue:`Gamma` #. :blue:`Chi-squared` #. :blue:`Truncated exponential` * :blue:`UCSD_UQ`: a. :blue:`Random variables (Priors)`: #. :blue:`Normal` #. :blue:`Lognormal` #. :blue:`Beta` #. :blue:`Uniform` #. :blue:`Weibull` #. :blue:`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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in interactive chart #. Scatter plot #. Histogram #. Cumulative distribution #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. Importance Sampling #. Gaussian Process Regression #. Polynomial Chaos Expansion b. Reliability: #. Local Reliability #. Global Reliability c. Sensitivity Analysis: #. MCS #. LHS .. dropdown:: 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.) #. **UQ (Uncertainty Quantification and Optimization Options)**: * Dakota: a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. :blue:`Importance Sampling` #. :blue:`Gaussian Process Regression` #. :blue:`Polynomial Chaos Expansion` b. Parameter Estimation: #. NL2SOL #. OPT++GaussNewton c. Inverse Problem: #. DREAM d. :blue:`Reliability`: #. :blue:`FORM` #. :blue:`SORM` e. :blue:`Sensitivity Analysis`: #. :blue:`MCS` #. :blue:`LHS` #. **FEM (Computational Model Specification)**: * OpenSees * FEAPpv #. **RV (Inputs to Computational Models)**: * Dakota: a. Random variables (UQ): #. Normal #. Lognormal #. Beta #. Uniform #. Weibull #. Gumbel b. 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 spreadsheet #. Update chart by clicking on spreadsheet columns * Output values visualized in interactive chart #. Scatter plot #. Histogram #. Cumulative distribution #. **Remote (Support for Analysis on DesignSafe's high performance super computer)**: * Dakota a. Forward Uncertainty Propagation: #. Monte Carlo Sampling (MCS) #. Latin Hypercube Sampling (LHS) #. :blue:`Importance Sampling` #. :blue:`Gaussian Process Regression` #. :blue:`Polynomial Chaos Expansion` b. :blue:`Reliability`: #. :blue:`FORM` #. :blue:`SORM` c. :blue:`Sensitivity Analysis`: #. :blue:`MCS` #. :blue:`LHS` We encourage new feature suggestions, please write to us at :ref:`lblBugs`.