A modular PyTorch library designed for learning, training, and deploying world models across various environments.

Curated papers, code, datasets, and benchmarks for medical world models in imaging, EHR trajectories, treatment planning, surgical AI, robotics, and virtual-cell simulation.

V-JEPA for Gray-Scott dynamics. Initial work produced during the 24-hour Hack the World(s) hackathon. 1st place 🏆

Demo implementations of JEPA World Models to support research

Conservative Lapse-Action Planning (CLAP): a variational access-and-dwell framework for safe AI optimization. Surf the conservative-lapse field to the best safe basin and dwell there — theorem-backed, pip-installable, with differentiable PyTorch training adapters.

Emergentia is a neural-symbolic discovery engine that extracts parsimonious physical laws from noisy particle trajectory data. It combines deep learning to model complex forces with symbolic regression to rediscover human-readable, mathematically interpretable equations of motion.

Analysis of premotor cortex signals in macaques during a countermanding task, using a PyTorch Deep Markov Model to generate and simulate neural activity in a low-dimensional latent space.

CARDIOKOOP - Control-aware Koopman deep learning framework for real-time hemodynamic forecasting and cardiovascular digital twin applications.