Component-Based Modelling Framework
Models are constructed using reusable physical components that represent real-world subsystems, supporting transparency and modularity.
Products
Innovate confidently with world-class systems simulation
SOFTWARE OVERVIEW
GT-SUITE is the industry-leading simulation tool with capabilities and libraries aimed at a wide variety of applications and industries. It offers engineers functionalities ranging from fast concept design to detailed system or sub-system/component analyses, design optimization, and root cause investigation.
MULTI-PHYSICS INCLUDED
The foundation of GT-SUITE is a versatile multi-physics platform for constructing models of general systems based on many underlying fundamental libraries:
GT-SUITE includes a state-of-the-art optimizer tool for performing design optimization and calibrating models to measured data. A variety of sophisticated and powerful search algorithms enable users to discover optimal designs within large, multi-dimensional domains. Explore design trade-offs among multiple competing objectives and constraints with the Pareto approach to multi-objective optimization. Parallel coordinates plots facilitate exploring inter-variable relationships, filtering data sub-spaces, and visualizing trends of multi-dimensional datasets. Finally, expedite long optimization runs by running multiple design iterations simultaneously via seamless integration with distributed computing.
GT-SUITE includes a visual-oriented machine learning platform that enables transforming data – whether it’s generated from simulations or taken from measurements and testing – into fast-executing metamodels. Metamodels are powerful tools for design exploration, sensitivity analysis, optimization, and replacement of computationally–expensive physics-based sub-models. Available metamodels include deep neural networks, Gaussian interpolation, and polynomial regression. Export metamodels to GT-ISE to use as fast surrogate predictions in any GT-SUITE model. When generating data from simulations, a variety of space-filling sampling algorithms are available to construct Designs of Experiments. User can also take advantage of machine learning capabilities using Python scripting with the GT-Automation add-on license.
GT-SUITE includes several approaches for sensitivity analysis and factor screening – including main effects plots, correlation coefficients, elementary effects method, variance-based Sobol method, and Monte Carlo filtering – that enable identifying and ranking the most influential input variables on any system. Similarly, identify negligible inputs that have little or no effect so they can be discarded from further analysis, thereby simplifying optimization and machine learning tasks.
In addition, GT-SUITE provides tools for performing Monte Carlo Variability Analysis, where statistical distributions can be applied to model inputs for the purpose of predicting variability in model outputs. Mathematical fits to the output distributions facilitate predicting risk of low-probability outcomes. Finally, as an engineering design tool, perform fast what-if studies to experiment with modifying input distributions to achieve more desirable output distributions.