INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER国际传热传质通讯

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER(英文缩写 INT COMMUN HEAT MASS),ISSN 0735-1933,eISSN 1879-0178,中文译名:国际传热传质通讯 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 最新收录文献

1. JCR分区: Q1 CAS分区: B2 影响因子: 6.2

   1. ResFouriONet: A Residual Fourier Operator Network with Synthetic Data Generation for Real-Time Laser-Induced Bioheat Transfer Modeling.

   1. ResFouriONet：一种结合合成数据生成的残差傅里叶算子网络，用于实时激光诱导生物传热建模

   PMID:

   41858567

   作者:

   Aditya Roy, Andrew DuPlissis, Adela Ben-Yakar

   期刊:

   International communications in heat and mass transfer PubMed

   DOI:

   10.1016/j.icheatmasstransfer.2025.109964

   日期:

   2025-11-04

   Accurate modeling of laser-tissue interactions is essential for applications such as multiphoton brain imaging and laser-based therapeutics, where precise thermal control is critical. However, traditional numerical solvers for bioheat transfer, especially when paired with Monte Carlo (MC) simulations for light transport, are computationally prohibitive for real-time use. While operator learning offers a scalable alternative, existing architectures often struggle with extrapolatory tasks and rely on large, labeled datasets that are costly to generate. In this study, we introduce ResFouriONet, a residual deep operator network tailored for modeling 2D transient bioheat transfer with parametric laser sources. Our architecture features a dual-pathway branch network comprising a Fourier sub-branch for capturing global thermal patterns and a cascaded residual sub-branch for localized heating, fused via a self-attention mechanism. To reduce the reliance on expensive MC data, we introduce a synthetic laser source function generation scheme that combines parameterized Gaussian profiles, exponential decay, and random field perturbations to emulate the complexity of MC-derived fluence maps. ResFouriONet achieves a 5× reduction in prediction error compared to vanilla deep operator networks, despite having fewer parameters. It attains an average relative error of 0.93% on unseen synthetic data and 1.45% on 630 zero-shot MC-generated source functions. Together, the proposed architecture and data generation strategy enable accurate, real-time prediction of laser-induced heating, paving the way for simulation-free planning and control in biomedical imaging and laser-based therapies.

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