This is a major release bringing notable enhancements to design exploration and predictive modeling tools, such as local search in surrogate-based optimization, SmartSelection improvements and more. It contains several new features which simplify certain routine operations, and adds a number of usability and interface improvements. Read on for more information on the release highlights.
The most significant update in pSeven 6.16 is the new optional local search algorithm added to the Surrogate-based optimization (SBO) technique in the Design space exploration block (enabled with the technique's Local search option). This is an adaptive algorithm, which intelligently limits the search scope in optimization, adjusting the SBO technique's complexity to the response evaluation time. Local search enables effective surrogate-based optimization in problems with a high number of variables and responses. In many tasks, it also reduces the overall time in solving due to better balancing between the internal analysis and response evaluation stages.
A few more notable updates to the Design space exploration block are:
• You can now configure the Surrogate-based optimization technique to extend the initial sample with just one design (next best candidate) by limiting the number of response evaluations to 1.
• The Adaptive design and Latin hypercube sampling techniques now by default perform design space normalization internally, which makes their behavior more stable.
• The updated Adaptive design technique better handles the cases where response evaluation failures can occur.
• Several useful updates to the Uncertainty quantification block: PDF and CDF data output ports, enhancements in handling HTML reports
- Internal improvements in SmartSelection algorithms to further increase model accuracy and training performance
- SmartSelection is now supported by the ApproxBuilder block to automatically select an approximation technique to obtain the most accurate model. This mode is now default
- Support for constrained model input domain in model training
- Approximation model block now outputs model information and validation data as a report in the HTML format, which you can view in Analyze or export to a file on disk.
- Approximation model can now load and evaluate data fusion models trained by the DFBuilder block. This enables data fusion model export to all formats supported by the block, including various source code formats.
pSeven 6.16 adds some functions aimed to simplify routine post-processing and data analysis operations:
- Composite blocks can cache their input and output data to a file on disk, although it is often hard to analyze this data because cache files use a specific format. This is no longer a problem because you can import the cached data to a report and process it with any tool available in Analyze. For example, load it to a Sample viewer, or export to Excel or CSV
- You can copy correlation analysis and variable importance data from a Sample viewer.
- The data import tool shows increase in performance when working with large Excel files. It also provides better table header recognition, and contains a few more usability tweaks.
Usability and Interface
This release adds various convenience features and improvements regarding interface responsiveness.
- Copy and paste values of inputs, outputs and parameters in Run.
- Copy values from a Sample viewer and paste them to the Inputs or Parameters pane in Run to set workflow's inputs or parameters.
- Test a script in the PythonScript block before running the workflow.
- And more
pSeven 6.16 also includes numerous smaller improvements and bugfixes – please see the release changelog for a full list. You can also contact us to get more information and pSeven updates.