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physics-informed-kriging-pro

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Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测

active-learningactive-samplingdeep-kernel-learningenvironmental-modelinggaussian-processgeostatisticsgpytorchkrigingmulti-fidelityphysics-informed
Creat：2025-08-23T17:15:48
Update：2025-08-30T15:11:33
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