AI RESEARCH
Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
arXiv CS.AI
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ArXi:2603.11045v1 Announce Type: cross 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.