Simulation data analytics

Simulation data analytics helps you turn complex data into clear actionable decisions through a simple step-by-step process:

• Centralize your data: store design variables, objectives, constraints, and metadata from each run in a single location using VOLTA.
• Explore relationships: use modeFRONTIER to plot variables against objectives, detect correlations, and identify non-linear behaviors between inputs and outputs.
• Identify trade-offs: visualize Pareto fronts, filter non-dominated solutions, and compare scenarios to find the best design trade-offs.
• Segment results: Apply clustering and filtering to group similar designs, filter by KPIs, and isolate promising regions of the design space.
• Track trends: Compare different studies and track KPI improvements over time across workflows.
• Build dashboards: Create interactive visualizations in VOLTA to share real-time insights with stakeholders and non-experts.
• Refine new designs: Feed your insights back into the loop to refine variable ranges, eliminate irrelevant parameters, and drive smarter DOE or optimization runs.

Yes, identifying key design drivers is one of the core benefits of simulation data analytics.

When running simulations in modeFRONTIER or managing them in VOLTA, data analytics helps you pinpoint which inputs drive your results — and which ones don’t.

With modeFRONTIER sensitivity analysis, you can:

• measure how much each variable influences the result,
• determine whether an effect is linear or complex,
• uncover hidden interactions between variables to rank them by importance.
  With VOLTA, you can:
• analyze data across multiple projects simultaneously,
• compare variable importance over time,
• share and reuse insights across different engineering teams to build long-term knowledge.

Visualizing high-dimensional simulation data with dozens of variables and outputs can be challenging, but simulation data analytics tools in modeFRONTIER and VOLTA offer a few practical ways to make sense of it.

• Combine scatter plots with smart filtering: scatter plots remain an essential tool when used strategically. In modeFRONTIER, you can pick two or three key variables at a time, apply filters and color points by a fourth variable. The trick is layering information rather than just plotting it.
• Use color, size, and filters to add dimensions: you can enrich existing charts. For example, use color to represent a performance metric, size to flag constraint violations, and filters to narrow down specific variable ranges.
• Leverage interactive dashboards: in VOLTA these visualizations become fully interactive. Users can explore data without changing the underlying plots.

• When objectives conflict — such as minimizing weight and maximizing strength — there’s rarely a single perfect design. Instead, simulation data analytics uses Pareo analysis to help you find the best possible compromise.
• Define your objectives: Establish what you want to minimize (cost, weight) versus what you want to maximize (performance, efficiency), while setting strict boundaries for limits (safety, temperature).
• Run DOE or optimization: use modeFRONTIER to collect enough design iterations to see how objectives interact.
• Visualize the Pareto front: plot your conflicting objectives against each other. The Pareto front shows designs where you can’t improve one performance metric without degrading another.
• Filter feasible designs: remove designs that violate constraints so you can focus on realistic options.
• Compare candidate solutions: use scatter plots, parallel coordinates to inspect each trade-off design.
• Share insights in VOLTA: store your analysis to securely manage results, compare studies, and collaborate on trade-off insights with your team.

Identifying clusters with simulation data analytics is one of the most effective ways to bring structure to large simulation datasets in modeFRONTIER and VOLTA.

Instead of looking at thousands of points, you group them into meaningful segments.

• Define similarity criteria: decide what you want to cluster. Grouping by inputs reveals similar geometries or parameters; grouping by outputs reveals similar performance profiles; combining both provides a balanced overview of your design space.
• Prepare your dataset: before clustering in modeFRONTIER remove invalid or failed runs, normalize variables, and filter to relevant designs (such as Pareto). This step ensures meaningful clusters.
• Apply a clustering method.
• Visualize the clusters
• Interpret what each cluster means
• Use clusters for decision-making. Once you understand them, you can: pick representative designs (centroids), focus optimization on promising clusters, and discard weak regions of the design space. In VOLTA, you can also: store cluster definitions, compare clusters across projects, and share insights with your team.

Yes, simulation data analytics is essential for validating simulation models. It helps you move from “the model runs” to “the model is reliable.” In modeFRONTIER and VOLTA, you can use simulation data analytics to check the accuracy, consistency, and robustness of your models.

• Compare simulation results with reference data.
• Analyze residuals and errors.
• Check sensitivity and robustness. A valid model should behave logically. Use modeFRONTIER to vary inputs slightly and observe output changes. If small input changes cause unrealistic jumps, something is wrong — smooth, consistent behavior builds trust.
• Detect anomalies and failed runs.
• Cross-validate across solvers or models.
• Validate surrogate models: compare predicted versus simulated values, check error metrics, and test on unseen data directly within the modeFRONTIER workflow.
• Track validation over time with VOLTA: store validation datasets, compare model versions, track improvements or regressions to make validation a continuous, automated process.

Yes, integrating simulation data analytics directly into your optimization workflows is highly recommended. In ESTECO tools, simulation data analytics isn’t a separate, disconnected step. It works before, during, and after optimization in modeFRONTIER, with all data managed in VOLTA.

An integrated workflow typically follows these steps:

1. run an initial DOE in modeFRONTIER
2. use simulation data analytics to identify key variables
3. run an optimization
4. use simulation data analytics to analyze Pareto fronts and trends
5. refine and repeat
6. store and collaborate in VOLTA

When managing large datasets, “best-performing” designs depend on your specific goals (single KPI vs. multiple objectives). Simulation data analytics helps you narrow down thousands of design points quickly in modeFRONTIER and seamlessly organize results in VOLTA.

• Constraint filtering to remove the noise.
• Use Pareto analysis for multi-objective problems. If you have multiple objectives, avoid picking a single design. Instead, automatically extract the Pareto front in modeFRONTIER and keep only non-dominated solutions. This step alone can reduce thousands of designs to a small, high-quality set.
• Rank designs for single-objective cases.
• Use visual filters to spot top regions.
• Apply clustering to group top designs.
• Define a composite score — optional but powerful.
• Save and track top designs in VOLTA. With VOLTA. Tag top-performing designs, compare across campaigns, and reuse them as benchmarks for future projects.