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PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics — arXiv:2609.33857

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33857
License (as archived): CC0-1.0
Archived: 2026-10-07T21:01:51.337Z
SHA-256 of the archived text: a9a5c21873f8321c75aeb55d319d21a517cbb350443a1eb9bf08a6a0b59fb12d
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How to cite

PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics — arXiv:2609.33857 — arXiv (en), https://arxiv.org/abs/2609.33857 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f2cda8a50ec9 (Metraipolis archive dc_f2cda8a50ec9, sha256 a9a5c21873f8321c75aeb55d319d21a517cbb350443a1eb9bf08a6a0b59fb12d)

BibTeX

@misc{dcf2cda8a50ec9,
  title = {PI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics — arXiv:2609.33857},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33857},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f2cda8a50ec9; sha256 a9a5c21873f8321c75aeb55d319d21a517cbb350443a1eb9bf08a6a0b59fb12d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33857. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recovering hidden transport mechanisms from sparse spatiotemporal observations is a fundamental inverse problem in scientific machine learning. In brain tracer imaging, dynamic contrast-enhanced MRI (DCE-MRI) provides time-resolved measurements of tracer concentration, while the underlying velocity and source mechanisms governing tracer propagation remain unobserved. We formulate this problem as physics-informed latent-state inference, in which the transport field itself is the primary object of inference rather than an auxiliary variable used only to reconstruct observed densities. We propose Physics-Informed Neural Optimal Mass Transport (PI-NOMT), a framework that represents density, velocity, and source as continuous neural fields and combines a continuous neural density teacher, recursive differentiable advection--diffusion--source rollout, unbalanced optimal-transport regularization, and governing-equation supervision. Physical laws act as structural priors that constrain the space of admissible transport mechanisms, while observed tracer dynamics provide evidence for estimating the latent transport state. We evaluate PI-NOMT on a synthetic benchmark with known ground-truth transport and on DCE-MRI sequences from nine control rats. On the synthetic benchmark, PI-NOMT accurately recovers the prescribed velocity field, including its magnitude, direction, and integrated trajectories, rather than merely reconstructing endpoint densities. Across the nine rat datasets, the framework yields sub-percent local endpoint error, consistent physical speed scales, and low post-training PDE and incompressibility residuals. These results support physics-informed latent-state inference as a general framework for recovering hidden transport mechanisms from observed dynamic scalar fields.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.33857 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:02.770Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).