Introduction
The 3DEXPERIENCE platform empowers engineers, researchers, and educators with a robust Design of Experiments (DOE) framework to explore, optimize, and validate designs in a systematic way.
Core Capabilities of DOE in 3DEXPERIENCE
Structured Experimentation – Define design variables, ranges, and objectives to generate scientifically structured test campaigns.
Automation – Automatically run large sets of simulation scenarios without manual intervention.
Integration with SIMULIA Apps – Seamlessly connect with Abaqus, CST, PowerFLOW, and other simulation solvers.
Visualization & Analytics – Explore response surfaces, correlation plots, and Pareto fronts to understand design trade-offs.
Optimization Ready – Directly couple DOE with optimization workflows for multi-objective and multi-disciplinary problems.
AI & Machine Learning Capabilities – the Approximation tool is the bridge between detailed simulations and fast decision-making, empowering engineers with ML-driven insights for design exploration and optimization.
The DOE framework is enhanced by Machine Learning (ML) models to accelerate decision-making:
Surrogate Modeling – Train ML models (e.g., response surface models, kriging, neural networks) on DOE results for fast prediction of new scenarios.
Design Space Exploration – Use ML-driven insights to identify hidden trends, nonlinear relationships, and optimal regions.
Predictive Design – Predict system behavior beyond simulated cases, reducing the number of expensive simulation runs.
Active Learning – Adaptive ML models guide the next set of experiments, ensuring smarter and fewer simulations.
Benefits for Users
✅ Reduce simulation cost & time
✅ Gain deeper insights into complex interactions
✅ Enable rapid “what-if” studies
✅ Build AI-ready workflows for continuous improvement
With DOE and ML, the 3DEXPERIENCE platform transforms traditional trial-and-error engineering into a data-driven, predictive, and optimized design process.
