这篇论文用PINO精确排名五种低成本墙体材料的热性能，数据少时比纯数据驱动方法更准，还能识别不同气候下的最优方案。搞建筑节能的可以看看。
该论文提出两阶段计算框架，用于热力学排名五种低成本本土墙体材料：泥砖、粘土-稻草土坯、石灰稳定竹板、烧制粘土砖和石灰-泥复合材料。第一阶段用Crank-Nicolson有限差分法模拟1500个周期日解；第二阶段用物理信息神经算子（PINO）学习参数到解的映射，相对L2误差5.14e-4，峰值内表面温度平均绝对误差0.201 K。PINO仅需150个FDM样本即可达到数据驱动FNO用300样本的效果。在名义炎热干燥夏季，粘土-稻草土坯获得最优成本-性能指数。气候扫描显示，在亚环境室外条件下排名反转，烧制粘土砖表现最佳。
A Physics-Informed Neural Operator for Thermal Ranking of Low-Cost Wall Materials in Hot-Dry Climates
Identifying cost-effective indigenous building materials that minimise heat penetration through walls is critical for indoor thermal comfort in low-income rural housing in hot-dry climates, where summer temperatures routinely exceed 45 C. We present a two-stage computational framework for thermal ranking of five low-cost indigenous wall materials: mud brick, clay-straw adobe, lime-stabilised bamboo panel, fired clay brick, and lime-mud composite. First, a validated Crank-Nicolson finite difference method (FDM) solves the one-dimensional transient heat equation with Robin boundary conditions under diurnal solar and outdoor air-temperature forcing, generating 1500 periodic-day solutions across a nine-dimensional parameter space by Latin Hypercube sampling. Second, a Physics-Informed Neural Operator (PINO) with a Fourier Neural Operator (FNO) backbone learns the parameter-to-solution operator mu -> T(x,t), enforcing both data fidelity and PDE consistency. The trained PINO attains a relative L2 field error of 5.14e-4 and a 0.201 K mean absolute error on the peak inner surface temperature, preserving the FDM material ranking exactly; PINO trained on 150 FDM samples matches a data-only FNO trained on twice as many, so the physics loss is most valuable when data are scarce. The periodic-day formulation also yields the ISO 13786 time lag and decrement factor, reproduced to within 0.99 h and 0.010. At nominal hot-dry summer conditions, clay-straw adobe achieves the best cost-performance index among widely available materials. A climate sweep, confirmed by FDM spot checks, reveals a regime boundary: under sub-ambient outdoor conditions the ranking inverts to conductive fired clay brick, delineating heat-exclusion and heat-rejection regimes. The framework supports evidence-based material selection for post-flood reconstruction in hot-dry regions.