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
pytorch spatiotemporal active-learning geostatistics kriging deep-kernel-learning gaussian-process multi-fidelity physics-informed gpytorch environmental-modeling active-sampling time-dependent-pde
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Updated
Aug 30, 2025 - Python