For AI Model Developers

Train on reality, not just simulation.

Read the FAQ
The challenge & how we solve it

We provide high-volume, cost effective, physics-grounded experimental datasets to train surrogate models, neural operators, and engineering foundation models.

Problem: The next generation of engineering AI depends on access to large, diverse, and trustworthy physics datasets. Yet most AI models are trained primarily on CFD-generated velocity field data, inheriting the assumptions, approximations, and biases of the underlying simulations.

High-quality experimental velocity fields datasets are scarce, often limited to simplified laboratory cases, two-dimensional measurements, or proprietary data that cannot be reused or benchmarked. As a result, developing robust surrogate models, neural operators, foundation models, and digital twins that generalize to real industrial processes remains a significant challenge.

Without experimental ground truth, AI surrogate models trained solely on CFD-generated data inherit the assumptions and limitations of the underlying simulations. This can reduce confidence in model predictions, particularly when extrapolating to complex industrial processes or safety-critical applications, ultimately limiting adoption at scale.

Fluidmapper bridges the simulation-to-reality gap by providing large-scale, experimentally measured 3D hydrodynamic datasets with quantified uncertainty. These physics-grounded datasets enable developers to train, benchmark, and validate surrogate models, neural operators, digital twins, and engineering foundation models using real industrial flow behavior, increasing model robustness and customer confidence.

Beyond velocity fields, Fluidmapper can synchronize experimental hydrodynamic measurements with process variables such as dissolved oxygen,kLa, torque, power consumption, gas holdup, temperature, pressure, and other customer-specific measurements. This richer, multimodal dataset enables AI models to learn the relationships between local flow structures and global process performance, delivering deeper engineering insights and more valuable industrial decision-support tools.

What you can do with it

Train surrogate models using experimentally measured 3D flow fields
Ground neural operators and physics-informed AI in real rather than simulated physics
Augment synthetic CFD datasets with experimental ground truth
Reduce simulation bias and improve the generalization of engineering AI models
Benchmark AI predictions against experimentally measured flow fields with quantified uncertainty
Train engineering foundation models on large-scale experimental hydrodynamic datasets
Develop digital twins grounded in experimental physics
Correlate local flow structures with practical engineering insights such as kLa, torque, power, dissolved oxygen, temperature, gas holdup, and pressure
License a growing library of experimental datasets to accelerate AI model development
Proof
  • 33 experiments in 35 days
  • 3D labeled confidence intervals
  • ML ready output format

What you receive

Large labeled time series datasets
Labeled VTU ML ready formats
Correlated process measurements datasets (kLa, temperature, pressure, gas flowrates etc)

Every engagement is fully managed — geometry preparation, tracer design, measurement, AI reconstruction, and delivery — with no capital equipment, specialized personnel, or long-term commitment.

Nature solves the equations.
We simply measure the result.

Send us your CAD model and operating conditions. Receive a feasibility assessment and quotation within 24–48 hours.