I’ve been experimenting with whether synthetic data can encode failure as a dynamical outcome rather than as a labeling rule.

The idea is to model a latent state vector () evolving under coupled stochastic dynamics,

dx = f(x) dt + σ(x) dW,

and emit observable variables downstream of these states. Regimes like failure, burnout, or collapse emerge from the dynamics themselves, not from thresholds applied to labels.

Across all datasets: – Latent states are integrated with RK4 for stability over long horizons – Positive feedback loops drive acceleration near failure – Regime transitions use hazard-based dynamics – After critical stress, system parameters change, enforcing hysteresis / irreversibility

I generated three open longitudinal datasets on Kaggle using this approach:

– Industrial pump failure (379k rows, 150 machines) – Human performance & burnout (975k rows, 140 agents) – Ecological stress & collapse (1.2M rows, 100 ecosystems)

I’d appreciate technical feedback on whether these dynamics look realistic or useful for modeling failure processes.

Links: * Pump dataset: https://www.kaggle.com/datasets/83dc6870bf12bc6181c0512fd95daf267100a2c637f8dfae05f8f37dc5db0371 * Human dataset: https://www.kaggle.com/datasets/1968d96cd9182959c0154a67c9ad6baf570e1221411f51c952ca12cf3bdc9f6d * Ecology dataset: https://www.kaggle.com/datasets/b40efa3b9b2026d769025e7055743872f94155ea99d49128beb65fe4b061e102