Deterministic Physical AI.
You can't trust an embodied system you can't reproduce. This program studies the digital twin as a verifiable, reproducible loop — fed by live IoT telemetry, rendered in the browser with WebGPU — so autonomy can be checked before and during deployment.
Four pillars, one loop.
IoT telemetry → digital twin → a deterministic sim/AI step → WebGPU render → a divergence check against reality, and back again.
The senses
Real devices stream pose, battery, LiDAR, and sensors over the wire; the twin ingests live state.
Embedded WebGPU
Rust/WASM + WebGPU render the twin at 60 fps in the browser — no app, no cloud GPU, on the device itself.
A live replica
A synchronized virtual copy of the asset and its world — runs forward to predict, backward to replay.
Reproducible loop
A seeded, fixed-timestep loop makes runs bit-for-bit reproducible — and flags when reality diverges.
What we don't yet know.
The research is defined by these questions — each one a gap between a twin that looks right and a twin you can trust.
How faithful is faithful enough?
What is the minimal twin fidelity — physics, contact, sensor noise — that still guarantees a task-level safety property? Over-modeling wastes compute; under-modeling breaks sim-to-real transfer.
Can the web compute deterministically?
WebGPU across GPU vendors and browsers is not bit-reproducible — floating-point order, fused multiply-add, parallel reductions all differ. What primitives make a deterministic, replayable physics step possible on the edge?
When do you trust the twin over the sensor?
Real-time pairing means bounding and detecting divergence between the physical asset and its virtual replica — and deciding, under noise and dropout, which one is right.
Can a full twin run on-device?
Turnkey pairing means the twin runs inside the edge device's own browser or webview — under its memory, GPU, and thermal limits, offline, with no install. What has to be true for that to hold?
How do you sync over a lossy link?
Telemetry is intermittent, out-of-order, and lossy. How should a twin ingest it — prediction, reconciliation, backpressure — and stay synchronized without drifting away from reality?
Can the twin calibrate itself?
Differentiable physics and sensor models could let a twin tune itself to reality by gradient descent instead of hand-fitting parameters. How far does that generalize across assets?
Is a twin run admissible evidence?
If a run is deterministic, reproducible, auditable, and signed, can it serve as 'digital evidence' for certifying an autonomous system — before and during deployment?
Open problems with leverage.
The questions above won't move without shared infrastructure. These are the highest-leverage gaps.
Reproducible web compute
Deterministic WebGPU / WASM building blocks for fixed-step, replayable simulation.
Fidelity & transfer metrics
Open benchmarks that score how well a twin predicts reality and transfers policies to it.
Telemetry & time-sync
Shared IoT schemas and clock synchronization so any device can pair with any twin.
Turnkey edge packaging
Self-contained WASM + WebGPU + OPFS bundles that deploy straight to a device's browser, offline.
A live demonstration.
The lab is the answer to Q4 made concrete: a working twin that deploys to the web with embedded resources — WebGPU rendering, live IoT telemetry, and a reproducible loop — to realize turnkey, real-time pairing with no install.
Open the Digital Twin lab
A full-bleed instrument: the WebGPU twin, a rendering dashboard, and a live telemetry feed — running entirely in your browser.
▶ Launch the live lab →