Surrogate Models Overview

Large-scale computational models are crucial tools to analyze complex systems. When coupled with uncertainty quantification and optimization methods, the resulting computational expense becomes intractable. In order to face the computational burden, surface approximation methods, black box models, or surrogate models are commonly used. FOQUS provides a selection of surrogate modeling tools all using a similar work-flow. This section provides an overview of the surrogate modeling features and capabilities. The details of each tool are provided in the tutorial sections.

The following surrogate modeling tools are currently available:

• ACOSSO – Adaptive COmponent Selection and Shrinkage Operator is a regularization method for simultaneous model fitting and variable selection based in nonparametric regression methods. ACOSSO is suitable for approximating models with many inputs and no sharp changes.

• ALAMO – Automated Learning of Algebraic Models for Optimization generates algebraic models from data sets. These surrogate models are ideal for equation oriented optimization problems (which are easily differentiable), such as super structure optimization.

• BSS-ANOVA – Bayesian Smoothing Spline Analysis of Variance is a method similar to ACOSSO.

• iREVEAL – Surrogate models for CFD simulations using Kriging or Neural Networks. It contains special features specifically designed for working with CFDs.

• Keras Neural Networks (keras_nn) – Surrogate models leveraging neural networks in TensorFlow Keras, a high-level machine learning library that supports training sequential or interconnected graph-based models.

• PyTorch Neural Networks (pytorch_nn) – Surrogate models leveraging neural networks in PyTorch, an optimized tensor library for deep learning that supports training sequential or interconnected graph-based models.

• Scikit-learn Neural Networks (scikit_nn) – Surrogate models leveraging neural networks in Scikit-learn, a predictive data analysis and surrogate modeling library that supports multi-layer regression and classification.

Data Selection

The Data tab allows the selection of training data to be used to generate a surrogate model (Surrogate Data Form). If the session is associated with a flowsheet data (results from a single flowsheet run, optimization runs, or UQ samples), then the flowsheet data is available to be the training data and the table will be populated accordingly.

Surrogate Data Form

1. Run the surrogate modeling method.

2. Stop the surrogate modeling method.

3. Surrogate modeling tool enables the user to select the desired surrogate modeling tool from the Tool drop-down list.

4. Description of the selected surrogate method.

5. Add Samples enables the user to generate new training data using a model specified in the flowsheet or an emulator (i.e., a basic response surface provided as part of the UQ module).

6. Flowsheet Results are summarized below.

7. The data table has a Menu drop-down list that contains display, import/export, and edit commands.

8. Select a data filter from the Current Filter drop-down for current data display.

9. Add or edit new data filters from Edit Filters. This dialog is shown in Figure Sort1 Data Filter Results.

10. The Display table displays the results of flowsheet evaluations stored in the FOQUS session file. The columns are:

    • SetName is a name assigned to samples. This is typically equivalent to one UQ sample run or one optimization run.

    • ResultName is a string representing a result name.

    • Error is the simulation result status; 0 indicates success, other numbers represent an error. A column for each node displays the error status of each node.

    • Time displays the time when the result was stored.

    • Elapsed Time describes how long a result took to calculate.

    • Tags enables a list of string labels to be applied to results. This could be used to mark results to be used for a particular purpose such as model validation.

    • The remaining columns display the input and output variables.

Filters can be used to select data. See Section Tutorial 4: Flowsheet Result Data for more information on creating filters to the results. The “All” and “None” filters are available by default. These can be used, for example, to assign all the data as a training set, or to split the data into a separate training set and a test set.

Variables

The Variables section is illustrated in Figure Surrogate Variable Selection. This section allows selection of input and output variables used in a surrogate model. Some surrogate methods such as ALAMO may generate and run additional samples while building surrogates. The Min/Max columns provide bounds on the variables. Selecting the checkbox next to the variable Name indicates that it should be included in the surrogate generation. Failure to select a checkbox for any variables will result in error during surrogate generation.

Surrogate Variable Selection

Note : The input and output variables that are displayed in this section are the ones present in the FOQUS flowsheet nodes. If the user would like to include additional output variables (calculated based on the original node variables) in the surrogate model, it is recommended to create them in the output section of the flowsheet node itself, from the very beginning. With this approach, the calculated variables will be a part of the surrogate variable selection, and their relation with the other node variables can be defined in the nodescript.

Method Settings

The Method Settings table is illustrated in Figure Surrogate Settings. The settings available in this table depend on the surrogate tool. A description of each setting is provided in the third column of the table.

Surrogate Settings

Execution

Clicking Run starts the surrogate model building process. The execution monitor displays after Run is clicked (see Figure Surrogate Status Monitor). The execution monitor displays the status of the surrogate build. The messages displayed depends on the surrogate tool.

Surrogate Status Monitor

After a successful execution and model building, the results are displayed. Note that in this case, the surrogate modeling tool ends with an error, the errors are displayed in this window. After surrogate generation completes, one or two Python files will be generated depending on the tool. Each tool generates a file that encodes the surrogate model as a general Python script that can be used to evaluate output values for UQ analyses within the UQ module. The other file, if available, is a FOQUS flowsheet plugin model that allows the surrogate to be run in a FOQUS flowsheet. The next version of FOQUS will generate a FOQUS flowsheet plugin model (i.e., the second file) for all surrogate tools.