Features
EPT-2 world model: physics-based atmospheric prediction
Athena agent: resolves physical objectives via simulation
Energy trading-specific forecast metrics
Prediction-market alpha generation (employee quant fund)
Research acceleration: experiment setup and data analysis
Transfer learning to other physics domains (e.g., aerodynamics)
State-of-the-art accuracy vs. ECMWF and other incumbents
Peer-reviewed research at ICLR and NeurIPS
Compounding loop: agent improves world model, world model improves agent
Supports atmospheric, aerodynamic, and thermal physics domains
Solar-powered rover with controlled illumination
Per-plant high-resolution imaging across growing season
AI-powered pest, weed, and disease detection per plant
Real-time plant growth monitoring
Automated yield forecasting from imagery and environmental data
Crop phenotyping via computer vision (leaf size, biomass)
Quality inspection for fruits and vegetables
Food waste reduction analytics
Integration of satellite, weather, and soil data layers
Multi-angle image capture with controlled rolling speeds
Machine learning to identify plant-environment interaction patterns
Data collection in muddy fields and bright sunlight
Terabyte-scale image datasets per field
Controlled rolling speeds and sophisticated sensors
