Architecture Overview

Platform Architecture

Cosmos Genesis is production-grade simulation infrastructure — not a research prototype. Three independent physics layers operate at planetary and interplanetary scale, grounded in NASA/JPL published standards and backed by cloud-native .

Three-Layer Simulation Stack

Each layer computes a distinct physical domain. They compose to support integrated scenarios — for example, a space weather event propagates through the orbital mechanics layer and produces terrain-level radiation exposure estimates in the surface layer.

Layer 1

Space Weather

Computes solar wind pressure, particle flux, electromagnetic spectrum, and geomagnetic field conditions at any point in the solar system. A planned live feed from NOAA’s Database Of Notifications, Knowledge, Information (DONKI) will keep space weather events current to within minutes, enabling simulations that respond to real coronal mass ejections and solar flare indices.

Radiation hazard assessmentSatellite drag modelingEM interference planning

Layer 2

Orbital Mechanics

Positions every solar system body, spacecraft, and satellite using ephemeris data derived from Spacecraft, Planet, Instrument, C-matrix, Events (SPICE) kernels, accurate to NASA/JPL published standards. Many-body gravity (N-body) integration propagates trajectories forward in time, while the satellite positioning engine ingests live two-line element set (TLE) data to track active constellations alongside historical mission profiles.

Mission trajectory planningSatellite constellation opsLaunch window analysis

Layer 3

Surface Operations

Planned layer, not yet built: sub-meter terrain fidelity from USGS Digital Elevation Models and SPICE Digital Shape Kernel (DSK) surface data for precision body-fixed frames, with line-of-sight (LOS) computation accounting for terrain occlusion to support field-of-view analysis for ground stations, emergency responders, and surface mission planning across planetary bodies.

Emergency services terrain (planned)Ground station coverage (planned)Surface mission planning (planned)

Coordinate System & Spatial Precision

Accurate positioning across interplanetary distances — while maintaining sub-meter surface fidelity — requires a deliberate numerical architecture. Single-precision floating point accumulates errors at planetary scales; the platform eliminates this class of error entirely.

Dual-Precision Floating Origin

Coordinate precision64-bit galactic / 32-bit local
Simulation unit1 SU = 1 km
Position error at Earth–Moon distance< 1 m
ArchitectureFloating origin — camera-relative local frame

World-space positions are maintained in 64-bit precision. The rendering engine operates in a camera-relative local frame using 32-bit math, eliminating floating-point jitter at all scales. This precision is required for emergency services terrain analysis and satellite positioning — applications where meter-level errors produce incorrect line-of-sight and coverage calculations.

SPICE Kernel Pipeline

All solar system body positions and spacecraft trajectories are derived from SPICE kernels — the same ephemeris standard used by NASA/JPL for active mission operations. Planetary surface-fixed reference frames (DSK) are on the roadmap; only orbital ephemeris (SPK) kernels are loaded today.

What SPICE Provides

  • →Solar system planet and moon positions
  • →Spacecraft and active mission trajectories
  • →Planetary body rotation and orientation frames
  • →Small body ephemerides (on demand)
  • ○Surface-fixed reference frames (DSK) (planned)

Kernel Coverage & Accuracy

  • →All eight solar system planets
  • →Major moons (60+ bodies)
  • →Active mission spacecraft
  • →Small bodies and near-Earth objects
  • →Accuracy: DE430 (NASA/JPL standard)

Ephemeris data comes from pinned NASA/JPL kernel releases (DE430 and satellite kernels). New upstream releases are adopted deliberately rather than tracked automatically.

Production Infrastructure

The platform runs on AWS production infrastructure at open-beta scale. The architecture is cloud-native and designed for on-demand — generation workloads scale horizontally to match simulation size.

Scale Claims

  • ✓4.3M-object generation run measured at 91.4s (46,945 objects/sec)
  • ○Trillion-scale generation: a cost/throughput extrapolation from that measurement, not a completed run
  • ○Sub-second spatial-hash bucket lookups measured at microbenchmark scale, not yet end-to-end against a trillion-object galaxy
  • ✓Open formats enabling cross-platform interoperability
  • ✓Cloud-native: scales across many machines on demand

Infrastructure Layer

  • GenerationMany machines in parallel — EMR Serverless
  • QueryIceberg data lake on S3 + Athena
  • APIECS Fargate (FastAPI)
  • DeploymentAWS — open-beta scale

Data Flow

The platform moves from physics generation to interactive visualization through a layered pipeline. Data is stored in open, interoperable formats at each stage.

Physics Generation
→
Data Lake
→
Query Layer
→
REST API
→
Visualization Client

Data is stored in open, interoperable formats at each stage of the pipeline — enabling integration with external analysis tools and ensuring long-term data portability.

Roadmap — Surface Operations

Layer 3 is planned, not yet available. The following are not implemented today:

  • ○USGS Digital Elevation Model ingest and elevation/profile queries
  • ○SPICE DSK surface-fixed reference frames for planetary bodies
  • ○Line-of-sight and terrain-occlusion computation
  • ○Field-of-view analysis for ground stations and emergency response scenarios
← Back to docs