Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Scientific Machine Learning · Depth: Expert, extended

Summary

ThermoField is a novel framework that infers spatially varying thermophysical properties from time-resolved thermal observations of complex 3D objects. Unlike traditional methods that either reconstruct temperature fields or estimate parameters under simplified conditions, ThermoField unifies these by employing differentiable heat-transfer simulation. It represents thermophysical quantities, such as thermal diffusivity (ranging 10⁻⁷ to 10⁻⁴ m² s⁻¹) and boundary heat-transfer coefficients, as neural fields on reconstructed object geometry. The framework jointly reconstructs geometry, estimates these properties, and predicts thermal evolution under unseen environmental conditions. Experiments on synthetic datasets, including simple and complex objects with diverse material properties (e.g., thermal conductivity 0.2 to 149 W/(m·K)), demonstrate its ability to recover physically interpretable fields that generalize across different heating and cooling scenarios, although parameter identifiability varies with scene complexity and excitation type.

Key takeaway

For Research Scientists developing physically grounded AI systems, ThermoField offers a robust approach to inferring material properties from thermal data. You should consider integrating differentiable heat-transfer simulations with neural scene representations to move beyond appearance-driven models. This enables predictive thermal simulations and more accurate digital twins, even for complex geometries, by directly estimating transferable thermophysical fields rather than just reconstructing temperature.

Key insights

Differentiable heat-transfer simulation enables inferring spatially varying thermophysical properties from thermal observations.

Principles

• Thermal observations encode heat-transfer physics.
• Spatially varying fields capture material heterogeneity.
• Differentiable physics constrains inverse problems.

Method

ThermoField reconstructs geometry, registers thermal data, represents thermophysical properties as neural fields, then optimizes these fields via a differentiable finite-element heat-transfer solver.

In practice

• Estimate thermal diffusivity for material identification.
• Predict object thermal behavior under new conditions.
• Monitor infrastructure for material degradation.

Topics

• Thermophysical Properties
• Differentiable Simulation
• Neural Fields
• Heat Transfer
• Thermal Imaging
• Digital Twins
• Inverse Problems

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.