Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
Inverse problems for stiff parabolic partial differential equations (PDEs), such as the inverse heat conduction problem (IHCP), are severely ill-posed: the forward map rapidly damps high-frequency interior structure before it reaches the boundary. Soft-constrained physics-informed neural networks (PINNs), which embed the PDE as a residual penalty, suffer from gradient pathology in this regime and tend to fit boundary measurements while leaving the interior field essentially untouched. We propose Neural Field Thermal Tomography (NeFTY), a hard-constrained neural field framework for label-free three-dimensional inverse heat conduction. NeFTY represents the unknown diffusivity as a continuou...
| Authors | Tao Zhong, Yixun Hu, Dongzhe Zheng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 13 May 2026 |
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