Learning on-the-fly quasi-stationarity diagnostics from low-dimensional surrogates

The paper introduces a new quasi‑stationarity diagnostic for accelerated dynamics methods, replacing traditional MCMC diagnostics with a coarse‑graining framework for killed Markov jump processes and a recurrent neural network trained on particle approximations of non‑linear effective flows. It provides uniform‑in‑time, non‑asymptotic bounds for approximating the true process’s probability flow without assuming reversibility, and demonstrates that the learned RNN diagnostic outperforms the MCMC approach in ROC analysis, is less conservative, fails less often, and offers an interpretable decision threshold.

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