arXiv Machine Learning August 12, 2026 By Batu Candan, Simone Servadio

Population-Aware Physics-Informed Neural Particle Flow for Robust Spacecraft Bayesian Navigation

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arXiv:2606. 10959v2 Announce Type: replace Abstract: Spacecraft navigation often requires Bayesian inference from sparse nonlinear measurements that produce curved, multimodal, or geometrically constrained posterior distributions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

multimodal

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Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update

arXiv:2606. 10959v1 Announce Type: new Abstract: Physics-informed neural particle flow (PINPF) learns a deterministic transport field that moves particles from a prior distribution toward a Bayesian posterior while enforcing the governing probability-evolution equation.

By Batu Candan, Simone Servadio

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Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction

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By Jaeyeong Lee, Wonmo Koo, Heeyoung Kim

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Generative Model Proposal based Particle Filtering for Data Assimilation

arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.

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Dynamic Online Processor-Native Inference for State Estimation

arXiv:2607. 12095v1 Announce Type: cross Abstract: Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models.

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benchmarks

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Wasserstein Residuals: Learning Gradient Flows from Population Dynamics

Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distributions driven by an energy functional.

benchmarks

arXiv AI

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