Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
Tao Zhong Yixun Hu Dongzhe Zheng Aditya Sood Christine Allen-Blanchette
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Abstract
We propose Neural Field Thermal Tomography (NeFTY), a differentiable physics framework for the quantitative 3D reconstruction of material properties from transient surface temperature measurements. While traditional thermography relies on pixel-wise 1D approximations that neglect lateral diffusion, and soft-constrained Physics-Informed Neural Networks (PINNs) often fail in transient diffusion scenarios due to gradient stiffness, NeFTY parameterizes the 3D diffusivity field as a continuous neural field optimized through a rigorous numerical solver. By leveraging a differentiable physics solver, our approach enforces thermodynamic laws as hard constraints while maintaining the memory efficiency required for high-resolution 3D tomography. Our discretize-then-optimize paradigm effectively mitigates the spectral bias and ill-posedness inherent in inverse heat conduction, enabling the recovery of subsurface defects at arbitrary scales. Experimental validation on synthetic data demonstrates that NeFTY significantly improves the accuracy of subsurface defect localization over baselines. Additional details at https://cab-lab-princeton.github.io/nefty/引用本文复制引用
Tao Zhong,Yixun Hu,Dongzhe Zheng,Aditya Sood,Christine Allen-Blanchette.Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation[EB/OL].(2026-03-11)[2026-03-13].https://arxiv.org/abs/2603.11045.学科分类
热力工程、热机
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