3Studio
Environment setup and directory-level agents work out of the box, bringing AI down into an engineer’s daily operations: design, verification, flow and operations, fully governed and auditable.
// 7DAWN · SINGULARITY DAWN
Self-evolving
physical AI
From commercial space into industrial design and manufacturing
The model keeps learning in real industry and gets more accurate with use.
SCROLL
Tens of days → days
Design-simulation iteration cycle · joint trials in the satellite domain
Full process
Design → simulation → manufacturing
Multiphysics
Structural / thermal / fluid / EM / electronics / control
Auditable · Reversible · Human-in-the-loop
The bottleneck in industrial design moved from
“can we compute it” to “can we verify it fast”.
Programs slip and tests fail because the design-verification cycle is the constraint, not compute. In the physical world a general large model is jagged: its world model is too coarse, like approximating a real field on a crude mesh. It only sharpens round by round on feedback from real simulation and measurement.
01Verify
Verification rests on people
One round of model → simulate → redesign, with a separate toolset for structural, thermal, fluid and EM, and a human wiring them together.
A round takes tens of days
02Expertise
Judgment rests on a few experts
The judgment behind a program lives in a handful of chief engineers and walks out with them; the next program starts over.
People leave, knowledge resets
03Frozen
A model ships frozen
A general large model is fixed at delivery. It does not get more accurate because it is used on this satellite or this engine.
No floor access, no improvement
Why now: international capital and domestic policy point the same way at the same moment — pushing AI from chat into the design and manufacture of physical systems. Industrial agents are now a national task with a numeric target, and landing them takes a local version that can get onto the floor.
One physical-AI foundation, three ways onto the engineer’s desk.
A desktop agent, a team platform, and the vertical model foundation — one base, three ways into the engineering floor.
Aboard
Treat agents as colleagues: assign work, track progress, raise blockers. People and agents share one board, organizational experience accumulates daily, and the knowledge base stays in sync with the desktop.
Vertical model foundation
Three model lines: structure generation, multiphysics simulation surrogates, and planning-and-solving. Deployable on-premise, with training data never leaving the customer environment.
5+
Workspaces
sec→mo
Self-evolution at 3 speeds
Full
Auditable · Reversible
6+
Multiphysics disciplines
— 3Studio · Workspace Switcher —
Illustrative dataLIVE · switch like Notion/Figma
Running · Design Session #DS-204
T-01Orbit simulation · 500km SSOpass
T-02Control-law designpass
T-03Parameter sweep · DOErun
T-04Cross-discipline interface checkwait
T-05Generate design package · Draftwait
Metrics
Active agents4 · Orbit / ATC / DOE / Doc
ExplorationMany in parallel · DOE
Design cycleweeks → days
StatusIllustrative run
Industry pack: Commercial Space · multiphysics toolchainOrbitOps = Operate + Commercial Space Pack
Physical AI = vertical models × physical ground truth × continual learning.
The three model lines emit executable structures and solver results, not prose. Every improvement has to clear physical ground truth: simulation and measurement, plus the optimality bound a solver proves for itself. Stages run across, multiphysics disciplines run down, unified on one foundation.
Concept
Detailed design
Simulation
DfM
Manufacturing
Structural
Thermal
Fluid
EM
Electronics PCB
Control
Manufacturing is a later phase
CAD/CAE toolchain in place: MATLAB / Simulink · STK · Ansys · Creo · HyperMesh · Nastran, extensible to third-party tools over MCP.
Governance built in: tool allow-list · full-chain audit · one-click rollback · tiered approval (human-in-the-loop).
More accurate with use —— self-evolution at three speeds.
Continual learning goes into the model, not into the prompt: seconds to adapt at inference, days to consolidate memory, months to train weights against simulation, measurement and solver optimality bounds.
Illustrative · more accurate with use
The reuse curve is the core metric of this route — for each new physics domain, how many simulation samples fine-tuning saves over training from scratch. (Capability evolves with deployment.)
Seconds · Inference-time adaptation
Adapts while reasoning, learning this specific program as it runs.
Days · Memory consolidation
Memory settles and hardens, carries across sessions, stays auditable and reversible.
Months · Ground-truth training
Simulation, measurement and optimality bounds act as reward, training the weights themselves.
Two gates · Regression + truth
General-capability regression past a threshold blocks the update; an improvement without ground truth to judge it does not count.
Along one chain of ground truth, from design verification into manufacturing.
We advance along the same CAE toolchain, and the physical ground truth gets harder the further we go. One foundation, from design through to production, with every step driven by real demand.
Now · on the floor
Satellite systems / structural / mechanics & thermal
SCOPE
Five engineering scenarios: systems, control, structural, mechanics and thermal, and MBSE.
DELIVER
Coupled multiphysics simulation with intelligent sampling compresses the design-simulation cycle to days; chief-engineer know-how settles into structured memory.
GAIN
The hardest physical ground truth, a verifiable evaluation protocol, and cross-discipline toolchain orchestration.
12 months
Simulation surrogates for engines and electric propulsion
SCOPE
Multiple ignitions, deep thrust variation, electric-propulsion thermal design.
DELIVER
Surrogate models over coupled structural, thermal, fluid and EM behaviour, with intelligent sampling to call the high-fidelity solver less.
GAIN
The second and third physics domains, and the first data points on the reuse curve.
24 months
From design verification into the plant
SCOPE
Manufacturability checks, process simulation, and the production impact of design changes.
DELIVER
Quality escapes traced back to the design; planning-and-solving takes over scheduling and dispatch.
GAIN
Aerospace-grade design and verification capability, handed to the manufacturing supply base.
Expanding along the customer pipeline
Aircraft OEMsState-machinery manufacturingSupply-chain planningEquipmentAuto componentsNew-energy supply base
Doing physical AI at the compute layer.
Others stop at the language and knowledge layer. We go into the compute layer and emit executable structures and solver results, with CAE and EDA tools as the engines we orchestrate underneath.
Gene
Three things stack up that are ours alone: a physical ground-truth data pipeline, a verifiable evaluation protocol, and access to the floor. The team combines large-model training, EDA commercialization and aerospace program engineering.
Market
Along the ground-truth chain from design verification into manufacturing — each new physics domain makes the reuse curve cheaper. Deep on the capability axis, wide on the industry axis.
— Talk to the 7dawn team —
Start from the hardest
factory floor.
High-end manufacturing teams are welcome to reach out — we work closely with you on requirements and delivery.
EMAIL
contact@7dawn.aiPHONE
+86 188 1030 8915