论文标题
基于物理物理的温室模拟
Differentiable Physics-based Greenhouse Simulation
论文作者
论文摘要
我们根据物理过程提出了一个可区分的温室仿真模型，可以通过从真实数据中训练来获得参数。基于物理的仿真模型是完全可解释的，并且能够在很长的时间内对温室中的气候和作物动态进行状态预测。该模型通过构建线性微分方程系统并解决它们以获得下一个状态来起作用。我们提出了一个程序来求解微分方程，处理数据中丢失无法观察的状态的问题，并有效地训练模型。我们的实验表明该过程有效。该模型在训练后显着改善，并可以模拟一个可以准确生长黄瓜的温室。
We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.
