Training, validation, test, and out-of-distribution (OOD) trajectories for the three physical systems used in Hybrid Neural World Models (Pranav Lakshmanan, Paras Chopra). The accompanying code, checkpoints, and paper define a single neural surrogate that predicts states at any horizon plus a step-doubling trust signal that flags when its forecasts can be trusted. This repo contains the raw trajectory data only. Models / training code live separately.… See the full description on the dataset page: https://huggingface.co/datasets/PraLak/Hybrid_Neural_World_Models.