Simulation and parameter optimization of the temperature field in mild moxibustion using physics-informed neural networks.

Authors

Honghua Liu, Zhenhua Fu, Jiayi Liao, Jiamin Hu, Mailan Liu, Chang She

Published in

Journal of thermal biology. Volume 140. Pages 104539. Jul 15, 2026. Epub Jul 15, 2026.

Abstract

Accurate prediction of transient tissue temperature fields is important for understanding the thermal response of mild moxibustion and supporting treatment-parameter screening. In this study, a two-dimensional axisymmetric physics-informed neural network (PINN) model was developed to simulate bioheat transfer during mild moxibustion. The Pennes bioheat equation, layered tissue properties, and coupled radiation-convection boundary conditions at the skin surface were embedded into the loss function. Nine L9 (3⁴) orthogonal cases were designed to evaluate the effects of moxa-burning temperature, moxa-stick diameter, moxibustion distance, and ambient temperature on the temperature response at a depth of 5 mm. The training processes showed stable convergence, and the average training time for a single parameter case was approximately 10 min. An additional confirmation case was compared with an independent COMSOL finite-element numerical benchmark at t = 1200 s. The mean absolute error, root-mean-square error, and maximum absolute error between the PINN and COMSOL temperature fields were 0.159, 0.299, and 2.723 °C, respectively. Range analysis showed that the influence of the four parameters followed the order of moxa-stick diameter > moxibustion distance > ambient temperature > moxa-burning temperature. The candidate parameter combination obtained under the present model conditions was 18 mm moxa-stick diameter, 25 mm moxibustion distance, 32 °C ambient temperature, and 650 °C moxa-burning temperature. These results suggest that the proposed PINN framework can provide a physics-constrained computational tool for temperature-field reconstruction and preliminary parameter screening in mild moxibustion, while further experimental and clinical validation remains necessary.

PMID:
42468079
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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