UniMaNO: Neural Operator-Based Unified Material Representations for Generalizable Thermal Modeling

Identifying material thermophysical properties from limited temperature observations and predicting subsequent thermal evolution are key goals of thermal modeling. Neural networks offer new approaches, but methods based on per-instance reconstruction still require independent optimization for each material and lack a unified representation that can be shared and transferred across materials. To address this limitation, we propose UniMaNO: Neural Operator-Based Unified Material Representations for Generalizable Thermal Modeling, a neural operator framework centered on a unified material representation. Specifically, UniMaNO maps temperature observations to material thermophysical property functions through a two-stage framework: first constructing a shared thermophysical function basis from training conductivity curves, then using temperature reconstruction errors from a differentiable heat conduction simulator to learn the observation-to-material representation mapping and refine material representations. After training, UniMaNO identifies the thermophysical properties of unseen materials without per-instance optimization, reuses the inferred representations for thermal evolution prediction under new geometries and heat-source conditions, and further supports few-shot refinement using the input observations. Extensive experiments demonstrate that UniMaNO enables rapid property identification and physically consistent thermal evolution prediction across materials and operating conditions.