Building Australia’s microgravity digital twin.

Challenge

Microgravity research has an accessibility problem: flight missions are rare, expensive and high-risk, and for most teams the gap between 1G on Earth and microgravity in orbit is a black box.

The tempting move is to train an AI on whatever biological data you can find and call the output a simulation of space. We think that produces confident nonsense. The harder question this grant funded was whether you could build a twin anchored in physics, from genuinely scarce public data, and then prove it against reality rather than against itself.

Solution

We built the engine in two halves. A rigorous physics simulator generates virtual experiments for 3-D biological growth. Its output trains a faster AI model, which is the part used to compare conditions repeatedly.

The critical design decision was the anchor. The combined system is checked against real, openly licensed biological growth data, not only against its own simulation. Without that, a twin can only prove it agrees with itself.

Technology

One scarce real biological dataset calibrated the physics simulator. That simulator generated virtual experiments across a systematic sweep of conditions, and those results trained the fast AI model. The result is lean enough to run on a laptop or low-power hardware.

Gravity enters as a tunable settling force: remove settling and cultures self-assemble round and cohesive; let it dominate and they flatten into a disk. That is the Gravity Knob, and it is the part of this work that points at space.

Results

Measured, not asserted:

• ~88% fidelity reproducing the physics simulation, head to head.
• 96% of the accuracy of a model trained directly on the real growth data, on a train-on-synthetic, test-on-real benchmark.
• Simulation-to-reality anchor error a small fraction of the growth it predicts, measured against real biological data rather than against the simulator.
• Lean by design. Orders of magnitude faster per sample than the physics simulator it learned from, and small enough to run on a laptop, or on low-power hardware in orbit.
• Calibrated uncertainty on every prediction, with confidence bands that run conservative rather than optimistic. That is the direction you want to be wrong in.

The number we’re proudest of is one that started badly. An early benchmark came in at 68%, well under target. We investigated it rather than tuning it, traced it to a root-cause limitation in the growth law, and redesigned that law. The benchmark rose to 96% while physical calibration simultaneously improved more than fourfold and the necrotic-core structure stayed intact. A metric-hack cannot do that; only a real fix moves all three at once.

Business impact

The grant took ALTDATA from a plausible pitch to a validated engine: the difference between describing a digital twin and running one. That engine is now the foundation under our biomedical Digital Twin work, and its deliberately small footprint keeps a data-sovereign, edge and in-orbit deployment path open rather than forcing everything through a cloud.

It also produced something less tangible and more useful: a validation discipline. We keep a three-bucket ledger that never blurs. What is validated against reality, what is corroborated premise, and what is an open roadmap gap. We ship the gaps too.

The Australian project and its next step

Be precise about the limit. The settling mechanism is validated against real 1G suspension-culture data, which shares the round, low-settling regime with true microgravity. The microgravity-specific delta (3-D biological growth under real µg against a matched 1G control) is not yet acquired, and not yet validated. The engine is built to report that it cannot self-certify there, and it does.

Closing that gap needs real flight data, and that work is now funded. The Round 3 project is led by ResearchSat (opens in a new tab), with ALTDATA and AICRAFT (opens in a new tab) as partners. The planned proof-of-concept mission will combine space-flown biological data, edge computing and ALTDATA’s hybrid AI models. The project was announced by the South Australian Space Industry Centre and the Defence Innovation Partnership. When those results land, this section gets the answer. Until the mission flies, the gap stays open and we’ll keep saying so.