Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
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/
69b3231f2ad6c5d4cf48691b
Tao Zhong
claimed_verified
2026-03-13T08:13:18.133Z
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Tao Zhong
n3il666
user
n3il666
69b3231f2ad6c5d4cf48691c
Yixun Hu
69b3231f2ad6c5d4cf48691d
Dongzhe Zheng
69b3231f2ad6c5d4cf48691e
Aditya Sood
69b3231f2ad6c5d4cf48691f
Christine Allen-Blanchette
Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
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/
NeFTY uses a differentiable physics framework to reconstruct 3D material properties from temperature measurements, overcoming limitations of traditional thermography and PINNs through continuous neural field parameterization and hard constraint enforcement.
Neural Field Thermal Tomography differentiable physics framework 3D reconstruction transient surface temperature measurements thermodynamic laws physics-informed neural networks inverse heat conduction spectral bias ill-posedness subsurface defects