We study an inverse problem for stochastic and quantum dynamical systems in a time-label-free setting, where only unordered density snapshots sampled at unknown times drawn from an observation-time distribution are available. These observations induce a distribution over state densities, from which we seek to recover the parameters of the underlying evolution operator. We formulate this as learning a distribution-to-function neural operator and propose BlinDNO, a permutation-invariant architecture that integrates a multiscale U-Net encoder with an attention-based mixer. Numerical experiments on a wide range of stochastic and quantum systems, including a 3D protein-folding mechanism reconstruction problem in a cryo-EM setting, demonstrate that BlinDNO reliably recovers governing parameters and consistently outperforms existing neural inverse operator baselines.
@article{arxiv.2511.12316,
title = {BlinDNO: A Distributional Neural Operator for Dynamical System Reconstruction from Time-Label-Free data},
author = {Zhijun Zeng and Junqing Chen and Zuoqiang Shi},
journal= {arXiv preprint arXiv:2511.12316},
year = {2025}
}