Overview

To effectively perform data driven design in a generation of big data

and highly advanced simulation environment such as digital twin,

a tool is required for engineers to help execute expert level design optimization

and analysis even without having particular knowledge or expertise.

AIDesigner is an advanced total design optimization solution based

on PIDOTECH’s archive of know-how and expertise of optimization processes.

AIDesigner not only performs optimization but also generates data for contribution analysis,

correlation analysis, and sensitivity analysis and exports a comprehensive optimization report

that can be understood even by engineers who are new to the field.

AIDesigner Features

1)Sequential Sampling and Metamodeling

AIDesigner Features

2)Autonomous Optimization process and Report generation

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Summary

Feature that provides a prediction model with target accuracy

through an elaborate process of sampling and generation based

on PIDOTECH’s machine learning methods.

Feature that performs multidisciplinary optimization that not only provides the optimal solution

but also various information on the reasoning of the design change

(design variables that have high contribution on the improvement of responses, correlation, or sensitive to the response).

Feature that generates a user-friendly report on optimization results that can be useful

for data storytelling with the help of data analytics and visualization techniques.

AIDesigner Expectations

Wider accessibility

Perform optimization and data analysis without the need of extensive background or expertise.

Man-hour reduction

Perform optimization, analyze data, and generate a report with a single click of a button.

Archive know-how through design guides

Users can gather expertise with quantified data instead of depending on experiences as design guides are provided for result analysis.

Engineering perspective data utilization

Establish a response prediction process using PIDOTECH’s machine learning techniques on simulation or experiment data.