Decades of published work, patents, and prior designs.
Total Physics Inc.
The engineering brain, at machine scale.
An AI that learns from engineering knowledge, prior simulation and physical measurement — then closes the loop against its own multi-physics solver. From intent to production-ready hardware.
ElectromagneticThermalStructuralFluidManufacturingCost
One coupled model
The approach
We are teaching a model to learn the way an engineer does.
An engineer reads, experiments, iterates, and keeps what they learn. We built that same loop in software: an engineering memory that holds everything it has seen, and a real physics solver to test every proposal against.
Three streams train the model: published engineering knowledge, prior simulation, and physical measurement. The model then proposes a design to a native multi-physics engine, which solves and verifies it and returns ground truth for the model to learn from. That cycle repeats.
Millions of solved runs, encoded as graphs and sequences.
Lab, field, and test-bench data from partners and clients.
Large engineering model
- Multi-physics
- Hardware
- Design
- Systems
- Manufacturing
- Electromagnetic
- Thermal
- Structural
- Fluid
- Manufacturability
- Cost
Every cycle makes the next guess better.
Schematic of the training loop.
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Read
Decades of engineering literature, patents and prior designs.
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Experiment
Prior simulation, encoded so the model can learn from it.
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Iterate
A closed loop against a native multi-physics solver.
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Retain
Every verified solve becomes part of what the model knows.
Why it works
It doesn't have to be right. It has to guess well.
A good engineer meeting a new problem doesn't compute the answer. They propose a starting point drawn from everything they have seen, then verify it. The model proposes; real physics decides.
Two shortcuts the industry is taking — neither closes the loop
Surrogate models
A network trained to imitate one solver on one geometry. A new one is needed for every material, condition and shape.
Breaks when the problem moves outside the training envelope — which is exactly when you needed it.
Orchestration wrappers
A scheduler that runs the same disconnected commercial tools in a better order. The physics underneath stays exactly as fragmented.
Breaks when you need a domain to inform another — and the vendors it wraps ship the same integration themselves.
What we do instead
Intelligence inside the physics
The model proposes a design; our own solver takes it to ground truth. There is no training envelope to fall outside of, because the physics is always actually solved.
- The model guesses. Drawn from everything it has seen, like a veteran engineer sizing up a new problem.
- The solver decides. A real multi-physics solve, not an approximation of one.
- The loop closes. Every verified result trains the next guess.
Guessing well is the skill. Solving exactly is the guarantee.
Coupled physics
One model. Every domain. Solved together.
Electromagnetics, thermal, structural, vibration, manufacturability and cost — resolved against the same geometry, inside the same loop, so a change in one is a change in all.
Electromagnetics1.74 T peak
Magnetostatic solve of flux density across rotor and stator.
Thermal168 °C hotspot
Winding and magnet temperature under continuous load.
Structural198 MPa · SF 1.59
Rotor bridge stress at maximum overspeed.
Vibration / NVHMode 2 · 1.24 kHz
Mode shape driven by the electromagnetic force wave.
Thermal · 3DΔT 11 °C coolant
Full 3D thermal field on the real geometry.
Deformationdeformation ×11
Deflection under radial Maxwell stress, exaggerated for display.
Performance97.4 % peak
Efficiency across the full speed–torque envelope.
Resonance24th × Mode 2
Excitation orders swept against structural modes to find crossings.
Representative output from internal development runs; figures are illustrative. These examples come from electric-machine work because that is where we started — the loop itself is domain-independent.
Intent to production
From a specification to something you can actually build.
The loop does not stop at a simulation result. It runs until the design satisfies the physics, the process window and the cost target at once — whatever the product is.
01
Intent
Performance targets, operating envelope, duty cycle, cost ceiling — and the constraints that actually bind.
02
Concept
A starting design proposed from everything the model has seen, rather than from a blank sheet.
03
Coupled solve
Every relevant domain resolved against the same geometry in one pass, not handed between tools.
04
Optimize
Multi-objective search across the whole loop, so performance and cost trade against each other honestly.
05
Manufacturability
Every feature checked against its real process window. Failures resolved before design freeze.
06
Production intent
A design, a bill of materials and a process plan that agree with one another.
Where it applies
Anywhere performance, thermal limits, manufacturability and cost are decided by the same few millimeters of geometry.
Electric machines & powertrain
Traction motors, generators, inverters and drive units.
Robotics & actuation
Torque density against mass, duty cycle against heat, in packages with no margin.
Aerospace & defense
Qualification-driven programs where every change carries a verification cost.
Power electronics & thermal
Switching losses, magnetics and cooling designed together instead of in sequence.
Energy & grid
Hardware built to hold its performance across decades of service life.
Industrial & precision machines
High-volume products where a fraction of a point of efficiency decides the program.
Who is building it
Built by engineers who shipped the hardware.
Our team has architected and productionized award-winning electric powertrains at volume, written finite-element code that runs inside the industry's standard simulation tools, and taken a hardware company from first principles to the public markets. We have lived every handoff this model removes.
50+
Patents & publications
20+ yrs
In commercial simulation
Billion-scale
Hardware programs shipped
Get in touch
We are talking to investors, design partners, and engineers.
If you fund frontier technical companies, build hardware where physics is the constraint, or want to work on this — we would like to hear from you.
Get in touch