Optimization studies with Pareto fronts, response surfaces, and convergence tracking.
Optimization studies find the best parameter values to minimize or maximize your engineering objectives. SimPilot runs simulations iteratively, learning from each result to guide the next experiment toward the optimum.
Single-objective optimization
Optimize one quantity at a time:
"Minimize the pressure drop across this heat exchanger by varying fin height (5-20 mm) and fin spacing (2-8 mm)"
The AI explores the parameter space, tracks the best result found so far, and converges toward the optimal configuration. You see a running best-so-far chart that shows improvement over iterations.
Multi-objective optimization
When objectives conflict -- lower drag vs. higher downforce, better heat transfer vs. lower pressure drop -- SimPilot finds the Pareto front: the set of solutions where no objective can be improved without worsening another.
"Optimize fin height and spacing to maximize heat transfer coefficient and minimize pressure drop"
Experiment execution
The AI runs simulations across the parameter space, evaluating both objectives for each configuration.
Pareto front construction
Non-dominated solutions are identified and plotted. Each point on the Pareto front represents a different trade-off between your objectives.
Trade-off analysis
The AI highlights key trade-off regions: the knee point (best balanced solution), the extremes (best for each individual objective), and clusters of similar solutions.
Response surfaces
From the collected simulation data, SimPilot generates response surfaces -- smooth approximations of how outputs vary across the parameter space. These let you:
Visualize the full optimization landscape in 3D
Identify flat regions (insensitive to parameter changes) vs. steep regions (highly sensitive)
Predict approximate results for untested parameter combinations
Spot local vs. global optima
Response surfaces are rendered as interactive 3D surface plots that you can rotate and zoom.
Convergence tracking
Throughout the study, a best-so-far chart tracks how the optimal value improves over iterations:
Rapid early improvement: The AI quickly finds good regions of the parameter space
Plateau detection: When improvement stalls, the AI adjusts its search strategy
Convergence criteria: The study concludes when improvement falls below a threshold or the iteration budget is reached
Recommended visualizations
Chart combinations for optimization studies
Optimization dashboards typically combine these chart types:
scatter3D: Pareto front with objective values on axes, colored by a third metric
line: Best-so-far convergence tracking over iterations
surface3D: Response surface over 2-parameter domains
parallel: All parameters and objectives on parallel axes -- trace solutions across the design space
Study configuration
You control the optimization by specifying:
Setting
Description
Parameters
Which inputs to vary and their ranges (min/max or discrete values)
Objectives
Which outputs to optimize and whether to minimize or maximize each
Constraints
Hard limits on outputs (e.g., "stress must stay below 250 MPa")
Budget
Maximum number of simulations to run
The AI handles everything else: sampling strategy, experiment sequencing, and result evaluation.