Abstract:The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filters offer an alternative to stochastic sampling, existing formulations usually yield stiff differential equations. Conversely, existing deep learning approximations typically treat the update as a black-box task or rely on asymptotic relaxation, neglecting the exact geometric structure of the finite-horizon probability transport. In this work, we propose a physics-informed neural particle flow, which is an amortized inference framework. To construct the flow, we couple the log-homotopy trajectory of the prior to posterior density function with the continuity equation describing the density evolution. This derivation yields a governing partial differential equation (PDE), referred to as the master PDE. By embedding this PDE as a physical constraint into the loss function, we train a neural network to approximate the transport velocity field. This approach enables purely unsupervised training, eliminating the need for ground-truth posterior samples. We demonstrate that the neural parameterization acts as an implicit regularizer, mitigating the numerical stiffness inherent to analytic flows and reducing online computational complexity. Experimental validation on multimodal benchmarks and a challenging nonlinear scenario confirms better mode coverage and robustness compared to state-of-the-art baselines.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.23089 [cs.LG] |
| (or arXiv:2602.23089v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.23089 arXiv-issued DOI via DataCite |
Submission history
From: Oliver Toro [view email]
[v1]
Thu, 26 Feb 2026 15:10:45 UTC (10,315 KB)
[v2]
Wed, 13 May 2026 11:02:58 UTC (8,011 KB)