This tutorial shows how to build an optimization workflow using the Calculator node for the computations, run it and analyze the results. The problem consists in the structural optimization of a beam section, with two objectives and two constraints. It is an alternative to the video tutorial series "First Steps with modeFRONTIER".
Your Learning Guide
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Step-by-Step Learning: Use modeFRONTIER for your Design Project
This tutorial focuses on the components of the Designs Space environment, their use and purpose. It shows how to create charts and change their appearance, explains the use of design categories, and gives an overview of tools such as Table Creation, RSM wizard, MCDM wizard and Multi-Variate Analysis.
This tutorial provides an overview of the functionalities of the Parameter Chooser tool implemented in most integration nodes, which is used for automatic workflow building. This tool detects the input and output parameters from the selected simulation model and transforms them into optimization variables, which can be used for the computation of optimization objectives and constraints.
This tutorial shows how to import experimental data in modeFRONTIER with the Table Creation Tool. Imported data can be used for different purposes, for example for response surface training, as the initial population for an optimization algorithm, or simply for statistical and other types of analyses.
This tutorial shows how to deal with a decision-making problem using the Multi-Criteria Decision Making Tool. MCDM in fact helps solve decision and planning problems involving multiple and generally conflicting requirements, enabling the decision maker to choose a reasonable alternative from a set of available solutions according to their personal preferences.
This tutorial shows some basic statistical tools and charts available in the Design Space environment, which enable you to assess the correlations between inputs and output and screen you problem in such way to discard unimportant variables. This may help you simplify the optimization problem and reduce simulation time, or improve the reliability and the accuracy of response surfaces.
Beyond the Basics: Master Extra features and Exploit All the Power of Optimization
In some cases modeFRONTIER licenses may envisage the use of the software by multiple users. This tutorial explains how set up such a "shared" license on a remote server and make it available to users whose computers can connect with it.
MVA tools are designed for analyzing data with many variables to identify patterns and relationships, reducing and interpreting such large datasets, and predicting data behavior based on such observations. This tutorial explains how to use the available Multi-Variate Analysis tools, i.e. Hierarchical and Partitive Clustering, Multi-Dimensional Scaling, Self-Organizing Maps and Principal Components Analysis, on practical examples.
This tutorial explains how to use the Scheduling Project node to solve a simple hierarchical optimization problem of a cylinder geometry. The main problem has been decomposed in two levels, where one optimization level has been nested in another and a direct dependency between the two has been created. For each step of the upper level a full optimization loop is performed at the nested level.
This tutorials explains how to use the Sub-Process node to debug and test a workflow subset separately from the rest of the workflow and run that subset in a loop for a defined number of times to avoid possible simulation failures.
This tutorials shows how to perform a detailed statistical analysis of an imported external database, i.e. visualize data distributions, monitor trends, find linear correlations and outliers, check the effects of some variable and perform statistical tests, as well as organize data in clusters according to their similarities.
This tutorial shows how to import data in modeFRONTIER and analyze them in a Six Sigma context. It focuses in particular on finding the most probable statistical distribution of data series, checking the fulfilment of the Six Sigma criteria and monitoring the behavior of data over time.