An AI tool specialized for engineering
Overview
Software that efficiently analyzes CAE and experimental data generated in engineering work using machine-learning methods.
By performing multi-objective optimization and cluster analysis using the built surrogate models, it can extract diverse solutions and visually analyze correlations between input data and output results.
It includes signal-processing features such as time-/frequency-domain feature extraction and FFT, as well as numerical computation functions essential for pre/post-processing in machine learning.
It is also valued for its flexibility in customizing the software to fit each customer’s challenges.
Features
Building surrogate models
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It provides rich pre-processing features—including automatic creation of training data, feature extraction, and outlier removal—to support building highly accurate surrogate models.
Optimization and cluster analysis
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Leveraging the very fast computation of surrogate models, it can instantly run parameter studies for tens thousands of cases, enabling exhaustive design-solution exploration.
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In addition, by performing multi-objective optimization and cluster analysis using the built surrogate models, it can extract diverse solutions and visually analyze correlations between input data and output results.
Combining multiple machine-learning methods
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With a graphical interface, you can combine multiple machine-learning methods and data analyses and define them as a single model set.
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An interactive GUI slider makes it easy to intuitively understand by visualizing relationships between inputs and outputs.
System Requirements
- Supported OS
- Windows 10 / Windows 11
- Data formats
- Universal File, Nastran/pch, Adams Request/mtx, CSV, etc.