MetricVision GmbH
Back to Projects

Training Data Generation

Scalable synthetic training datasets for machine learning projects where real data is limited.

A B2B reference: we design generation pipelines that complement or substitute costly real-world capture when it makes sense.

Training data generation in brief

Training data generation produces labelled or structured datasets algorithmically—often synthetically or via simulation—so models can learn without depending only on manual acquisition.

Machine learning needs volume, variety, and reliable ground truth. When measurements are scarce or expensive, generated data accelerates iteration and shortens time-to-experiment.

Used well, it scales to very large batches, reduces the cost of repeated campaigns, and lets you dial in controlled variability (geometry, load cases, noise) that is hard to reproduce in the field alone.

Key advantages

  • Efficient generation of many variants without repeated physical test campaigns or full-scale sensor rollouts.
  • High reproducibility through deterministic pipelines, versioned parameters, and documented random seeds.
  • Target rare or extreme scenarios—edge loads, defects, outliers—that seldom appear in operational data but matter for robust models.

Limitations to plan for

  • When real measurements are missing or only partially available, models may absorb simulator bias instead of the physical world—domain gaps must be tracked and mitigated.
  • Synthetic data demands disciplined validation: independent checks, hold-out real samples, or expert review so labels and governing physics remain trustworthy.
  • Initial measurements, pilot captures, or calibration runs usually anchor the generator—pure synthesis rarely replaces every real-world observation.

Two concise examples

Example 1: FEM simulation data

Finite-element models discretise structures or thermal systems into meshes with materials and boundary conditions. Result fields—stress, temperature, displacement—can be exported per scenario. Rendered as images, tensors, or derived features and paired with labels, they become high-volume training data for surrogate models or classifiers without fabricating each physical specimen.

FEM simulation result showing mechanical stress magnitude on a structural bracket
FEM simulation of mechanical stress on a structural bracket.

Example 2: Physical simulation

Physics-based engines reproduce motion, contact forces, flow, or granular behaviour (e.g. powder dynamics). Sweeps over friction, particle size, or energy input yield diverse trajectories. Those sequences train ML models to generalise across settings while staying tied to conservation laws encoded in the simulator.

Short clip: powder-like dynamics in simulation.

Discuss your data strategy with us

Contact Us