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PPIM: Pennes Physics-Informed Mamba for Heat-Source-Conditioned 3D Bioheat Simulation — arXiv:2609.06869

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.06869
License (as archived): CC0-1.0
Archived: 2026-10-16T13:57:29.237Z
SHA-256 of the archived text: 399711195d1d1942379d72e71d21e37014aa3077c6ab1020debeadefe409fbae
Stable URL of this page: https://metraipolis.com/documento/dc_26419df80b63

How to cite

PPIM: Pennes Physics-Informed Mamba for Heat-Source-Conditioned 3D Bioheat Simulation — arXiv:2609.06869 — arXiv (en), https://arxiv.org/abs/2609.06869 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_26419df80b63 (Metraipolis archive dc_26419df80b63, sha256 399711195d1d1942379d72e71d21e37014aa3077c6ab1020debeadefe409fbae)

BibTeX

@misc{dc26419df80b63,
  title = {PPIM: Pennes Physics-Informed Mamba for Heat-Source-Conditioned 3D Bioheat Simulation — arXiv:2609.06869},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.06869},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_26419df80b63; sha256 399711195d1d1942379d72e71d21e37014aa3077c6ab1020debeadefe409fbae},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.06869. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Three-dimensional bioheat simulation aims to predict transient temperature distributions in biological tissue and is commonly modeled using the Pennes bioheat equation, which combines thermal diffusion, perfusion-mediated heat loss, and external heat generation. In this study, we consider a controlled 3D Pennes bioheat simulation under a localized heat-source condition inspired by microwave ablation (MWA). To evaluate neural approximation performance, we compare three neural partial differential equation (PDE) solvers under the same controlled simulation: a spatial Fourier-feature physics-informed neural network (PINN), a generic PINNMamba temporal subsequence model, and Pennes Physics-Informed Mamba (PPIM). PPIM builds on the temporal subsequence model by incorporating conditioned heat-source input and Pennes-aware state-space model (SSM) decay initialization. All three neural models are trained under the same conditions with the same Pennes residual, and an explicit finite-difference method (FDM) solution is used only as the numerical reference. In a representative 600~s run, PPIM achieved the lowest MAE, relative $L_1$ error, and relative $L_2$ error among the evaluated neural solvers. Error maps further showed that the remaining PPIM errors were more concentrated near the heat-source region than across the rest of the domain. These results indicate that PPIM is effective for approximating the FDM reference final temperature field in this controlled simulation. The source code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Mathematical Physics (math-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.06869 — 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 2026-10-16T13:39:42.913Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).