Signals are fragmented before decisions become difficult.
A wave forecast can say little about water chemistry. A pollution reading can say little about port exposure. A beautiful map can still hide where a number came from. Aethera starts with one disciplined question: can every visible output travel with its source, assumptions, formula and decision boundary?
The product is not the animated ocean. The product is a scenario that another person can understand, reproduce and challenge.
Wave behaviour comes from the selected state.
The initial laboratory uses deep-water approximations suitable for an engineering demonstration. For density ρ = 1025 kg/m³, gravity g = 9.81 m/s², significant wave height Hs and energy period T:
Energy E = ρ × g × Hs² / 8
Power P = ρ × g² × Hs² × T / (64π)
The shader receives the same Hs, T and wind state used by the calculation engine. It amplifies vertical scale so a reviewer can inspect the difference between scenarios; the amplification is visual, not a claim about geographic bathymetry.
No mysterious “AI score”.
The current stress value is a declared weighted heuristic: temperature anomaly 18%, dissolved-oxygen deficit 24%, chemistry deviation 16%, plastic pressure 18%, and wave/wind/current exposure 24%. The interface ranks these components and exposes the state that produced them.
A later machine-learning component is allowed only after authoritative, time-aligned and labelled evidence is available. Its validation set, error, uncertainty and model card must be published alongside any claim.
Static-first now; evidence-first at scale.
↓
Schema validation + source/time/quality flags
↓
Physics engine · environmental model · future ML service
↓
3D scene + explanation + evidence ledger
↓
Human review · export · audit
The current baseline is deliberately static-first: browser computation, no database and no always-on server. A pilot can later introduce a geospatial store, signed observations and low-cost serverless APIs without rebuilding the presentation layer.
Build around a confirmed problem statement.
- Confirm the official SIH statement and beneficiary with the institution.
- Select one pilot coast and define the decision the prototype must improve.
- Register authoritative data sources and an offline-safe sample dataset.
- Calibrate the physics/environmental baseline against observed records.
- Add one justified ML task—classification, forecasting or anomaly detection—not “AI everywhere”.
- Conduct usability, accessibility, mobile and low-bandwidth tests with evidence.
- Prepare a three-minute product story, technical demo and limitations response.
What this release does not claim.
It is not connected to a physical buoy, ship, port or government warning system. It is not calibrated to a specific coastline. It cannot issue evacuation, fishing, weather, navigation or environmental-compliance guidance. Sample records are controlled demonstrations.