Aether digital twins.
A twin is just a model that computes the world forward.
Most “digital twins” are dashboards bolted onto a one-time simulation. A real twin rolls a system forward in time and stays synced to reality — which is exactly what Aether was trained to do. Build a living twin of any system from the CAD, sensors and docs you already have; keep it honest with data assimilation; run what-ifs, inverse design and predictive maintenance against it. This is the capability under every Aether discipline — exposed directly.
- Agents
- 12
- Path
- Asset → Living twin
- Synced
- By data assimilation
- Tiers
- Surrogate → Real-time → High-fidelity → DNS
The whole twin lifecycle, under one model.
The incumbent twin stack is a simulation suite, a separate twin platform, a surrogate pipeline and a historian that never talk. Aether is one model and one twin — built so the physics that designs the asset, the data that syncs it, and the what-if that protects it all share the same weights.
Twin from spec & data
Build a twin from what you already have — CAD and BoMs, P&IDs, sensor histories, design docs. Aether assembles the geometry, the couplings and the parameters into one forward-rollable model, not a static dashboard.
Forward rollout in time
The defining act of a twin: roll the system forward and watch it evolve. Aether computes the world forward because it was trained on simulation, not scraped text — the same weights that describe a system can advance it.
Multiphysics coupling
CFD, FEA, electromagnetics, thermal, chemistry and controls coupled on one mesh — the cross-domain interactions a single-physics twin silently drops are exactly where real systems fail.
Data assimilation
Keep the twin honest: fuse live sensor streams to correct drift, so the twin tracks the real asset instead of diverging from it the day after commissioning.
Calibration & validation
Tune the twin against measured reality and report the residual — a twin that hasn't been validated against data is a rendering, and Aether labels it as one until it isn't.
Uncertainty quantification
Every twin output carries calibrated uncertainty and its provenance tier, so you know which predictions to trust and which need a higher-fidelity run.
What-if & scenario play
Branch the twin: change a setpoint, a load, a geometry, an attack, a policy — and forward-roll each branch to compare outcomes before committing one to the real system.
Surrogate acceleration
Distil a fast surrogate from the high-fidelity twin for real-time and many-query use — control, monitoring, optimization — with an honest accuracy bound back to the parent model.
Inverse design on the twin
Run optimization against the twin instead of the asset: target an outcome and let the model search geometries, parameters and controls under hard constraints.
Predictive maintenance
Forward-roll degradation — fatigue, fouling, wear, drift — to predict failure before it happens, and tell a real fault apart from a sensor problem by reasoning against the physics.
Cyber-physical twin
Couple the physics twin with a model of the network and controls, so a twin can answer security questions too — the basis of the cyber-physical discipline.
Provenance & versioning
Every twin is versioned and hashed; every prediction traces to the model, the data and the assumptions behind it, so a twin is auditable, not a black box.
Every twin knows how real it is.
The governing principle: a surrogate is never presented as high-fidelity, and an unvalidated model is never presented as a twin. The four-tier ladder and the validation residual make that structural — a rendering and a living twin are never confused.
Reduced-order surrogate
Fast distilled models for real-time and many-query use. Milliseconds. For monitoring and control loops — always labelled with its accuracy bound back to the parent.
Real-time twin
Interactive multiphysics at the fidelity most operational twins run in — fast enough to sit beside a live asset and stay synced by data assimilation.
High-fidelity twin
Full-resolution coupled multiphysics validated against measured data. The tier behind a design decision or a consequence you have to be right about.
First-principles (escalation)
Direct numerical simulation, DFT, detailed FEM/CFD. A regime the model cannot yet resolve returns the engine it requires — never a fabricated rollout dressed up as a twin.
Six system families, one twin model.
Not a different twin platform per domain — one model whose physics changes per system but whose contract does not.
Turbines, pumps, airframes, engines. Coupled FEA and CFD twins that predict fatigue, flow and thermal behaviour — and stay synced to the rig.
Boards, packages and chips. Electro-thermal and signal-integrity twins from the same model that runs the silicon flow.
Generation, substations and feeders. Power-flow twins that forward-roll stability and, coupled to the network, security consequence.
Reactors, bioprocesses and assays. Kinetics and transport twins that close the loop with the autonomous-lab discipline.
Manipulators, humanoids and fleets. The world-model twin embodied policies train and transfer through.
Process lines and facilities end to end — many coupled subsystems as one twin, with provenance an operator and an auditor can both follow.
A living twin, not a rendering.
The difference between a twin and a demo is whether it tracks reality and whether it tells you when it can't. We report both.
Trained to compute the world forward, not describe it
Live sensor fusion keeps the twin synced to reality
Surrogate → real-time → high-fidelity → first-principles
Every output carries calibrated uncertainty
Validated against measured data, or labelled as not
Versioned, hashed and fully auditable
One model for every twin.
A simulation suite, a twin platform, a surrogate pipeline and a historian collapse into one model, one contract, one provenance trail.
A living twin, not a rendering.
The simulation vendors twin one physics; the IoT clouds sync data but don't forward-roll it. Aether does both — a multiphysics twin synced to reality, with calibrated uncertainty.
Decisions, moved into the twin.
What compounds is how many calls you make in simulation instead of on the asset.
Mechanical, electronic, energy, biological, embodied — one simulation-native model instead of a different twin platform per domain.
Surrogate → real-time → high-fidelity → first-principles. Every twin query carries its tier, provenance and calibrated uncertainty.
Data assimilation keeps the twin tracking the real asset — the difference between a living twin and a one-time rendering.
A twin that reasons, not a dashboard.
It builds the twin, picks fidelity, keeps it synced, runs the what-if, and writes the rationale — for an expert audience.
- 01
Builds the twin
Ingests the CAD, the sensors, the docs and the operating history, and assembles a forward-rollable, multiphysics-coupled model before any query runs.
- 02
Picks the right fidelity
Knows when a real-time surrogate suffices and when a decision needs high-fidelity or first-principles — by the confidence the conclusion requires.
- 03
Keeps it honest
Fuses live data to correct drift and reports the residual against measured reality; a twin that hasn't been validated is labelled as a rendering, not trusted.
- 04
Runs the what-if
Branches the twin, forward-rolls each scenario, and quantifies the difference with calibrated uncertainty — so the decision is made in sim, not on the asset.
- 05
Writes the rationale
Each prediction ships with its fidelity tier, the data it assimilated, the assumptions it made and the model-version hash — auditable end to end.
- 06
Closes the loop
Measured outcomes return into the twin and sharpen the next rollout, so the model that predicted a system gets better at predicting it over time.
A twin that gets truer with every measurement.
Each measured outcome returns into the twin and sharpens the next rollout, so the model that predicted a system this quarter predicts it better the next — and the twin and the asset converge instead of drifting apart.
“The old twin drifted off the machine in a month. This one stays on it — and it told us about the fouling before the turbine did.”
Stand up a living twin of your system.
Aether builds a forward-rollable, multiphysics twin from what you already have, keeps it synced to reality, and runs the what-ifs you'd never risk on the asset. Request access to deploy it behind your firewall, or see how it powers the rest of the platform.
Digital twins are the capability under every discipline — one model, every system.