Motion
Dynamics, trajectories, maneuvering.
We rebuild how physical systems are modeled, understood and controlled.
To understand one physical system, teams still spend heavily on experiments, computation, expert time and repeated calibration.
The cost slows decisions before the right model even exists.
Build a useful model of a complex physical system, starting with the decision it must support. Use the simplest combination of physics, data and computation that can do the job.
We start with physical constraints to narrow what must be estimated. Then we test what sparse observations can identify, fit only supported dynamics, and check the model beyond the fitting data.
Explore Model StudioA model useful for understanding, optimization or control—with its limits made clear.
Start with existing test, simulation or operating data, physical constraints, and the decision the model must support.
Specify the quantity to predict, the operating conditions and the error the decision can tolerate before fitting a model.
If the data cannot identify a quantity, we stop fitting it. We state what is missing and which feasible observation could resolve it.
We use whatever works.
AI is a tool. Physics is the work.
Our starting point is the physics of your problem, rather than a predefined industry or software category.
Dynamics, trajectories, maneuvering.
Flow, forces, pressure.
Thermal systems, cooling, transfer.
Loads, deformation, failure.
Friction, grasping, interaction.
Conversion, storage, efficiency.
Feedback, planning, operating choices.
Research experience across hydrodynamics, CFD, reduced-order modeling, flow prediction and maneuvering dynamics informs how we frame a problem and test a model.
What's the system, where does the current process break down, and what decision should a better model make possible?
No pitch deck needed. Start with the problem.