Geospatial Analytics, Learning, and Intelligence for Land, Environment & Oceanography
Enterprise-grade AI-enhanced space-based geophysical sensing platform. Comprehensive, production-ready solution for orbital dynamics, guidance/navigation/control (GNC), geophysical inversion, and machine learning designed for autonomous satellite-based gravimetry missions.
Explore the complete source code, documentation, and deployment guides. Built with Python, JAX, FastAPI, Next.js 14, and CesiumJS for production-ready deployment.
View on GitHubHigh-Precision Space Mission Modeling
High-fidelity orbit propagation with J2, atmospheric drag, and solar radiation pressure perturbations. Supports LEO to GEO missions.
Interactive DemoMulti-satellite formation keeping with relative motion dynamics, collision avoidance, and optimal fuel consumption.
Interactive DemoGenerate realistic gravimetry measurements with configurable noise models, sensor characteristics, and orbital parameters.
Interactive DemoAutonomous Satellite Control Systems
Linear Quadratic Regulator and Gaussian controllers for optimal attitude and orbit control with guaranteed stability margins.
Interactive DemoMPC implementation with constraint handling for fuel-optimal maneuvers and real-time trajectory optimization.
Interactive DemoState estimation with sensor fusion, orbit determination, and real-time navigation using GPS, star trackers, and IMUs.
Interactive DemoPhysics-Informed Neural Networks for Space Applications
PINN architectures that encode orbital mechanics equations as soft constraints for improved generalization and physical consistency.
Interactive DemoConvolutional neural networks for denoising gravimetry measurements and enhancing signal-to-noise ratio in challenging environments.
Interactive DemoDeep RL agents for autonomous spacecraft control, adaptive maneuver planning, and fault-tolerant operations.
Interactive DemoAdvanced Inversion and Earth Models
Robust inversion algorithms with L1/L2 regularization for ill-posed gravity field recovery problems.
Probabilistic gravity field estimation with uncertainty quantification and posterior sampling.
Integration with standard Earth gravity models for reference field computation and validation.
Seasonal water mass variation corrections for improved temporal gravity field resolution.
Multi-Objective Optimization Framework
| Capability | Description | Output |
|---|---|---|
| Design Space Exploration | 1,000+ configuration analysis with automated parameter sweeps | Trade Study Reports |
| Multi-Objective Optimization | Pareto-optimal solutions for cost, performance, and risk | Pareto Fronts |
| Risk Assessment | Monte Carlo simulations for mission success probability | Risk Matrices |
| Cost Modeling | Parametric cost estimation with uncertainty bounds | Budget Projections |
| Timeline Planning | Mission phase scheduling with critical path analysis | Gantt Charts |
Enterprise-Grade Security Infrastructure
Role-based access control with fine-grained permissions for multi-tenant deployments.
Tamper-proof audit trails with cryptographic signatures for regulatory compliance.
GDPR, CCPA, HIPAA, SOX, and PCI-DSS compliance infrastructure built-in.
Production-Ready Full-Stack Deployment
Async Python API with Celery for distributed task processing and background jobs.
Modern React framework with server components and CesiumJS 3D globe visualization.
Time-series optimized database for orbital telemetry and measurement storage.
Real-time monitoring dashboards with alerting for mission-critical operations.