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environmental-modeling

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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

  • Updated Aug 30, 2025
  • Python

Comparing LightGBM, a vanilla neural net, and a Physics-Informed NN for ETo estimation. The PINN embeds FAO-56 physics in its loss; reproducible synthetic data show physics helps most when measurements are scarce or noisy. Ready-to-run code.

  • Updated Aug 2, 2026
  • Python

Advanced framework combining 2D contaminant transport simulation (advection–diffusion) with machine learning to classify ecological risk (Low/Medium/High) and generate dashboards, GIFs, and reports.

  • Updated Jun 27, 2026
  • Python
ADR1D-Explorer

Bilingual web application for exploring validated one-dimensional reactive-transport simulations and comparing analytical, finite-volume, and neural solutions.

  • Updated Aug 3, 2026
  • JavaScript

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