用机器学习加速海洋生态模型稳态预测，节省50%~95%计算时间。
Prediction of steady states in a marine ecosystem model by a machine learning technique
- 用条件变分自编码器学习参数到稳态的映射，加入质量修正。
- 预测结果作为初始值，使模型迭代次数减少50%至95%。
- 适合需要快速启动海洋模型的研究者，尤其关注计算效率者。
我们利用全球海洋生态系统模型通过预运行得到的稳态数据作为训练集，构建从少量生物地球化学参数到三维收敛年周期稳态的映射关系。该映射采用带质量修正的条件变分自编码器（CVAE）实现。将模型应用于测试数据时，发现CVAE预测的稳态已能合理逼近常规预运行结果，但其年度周期性略弱。因此，我们将预测结果作为预运行的初始值，显著降低了达到预定停止准则所需的迭代次数（对应模型年数）。根据停止准则不同，计算时间节约率达50%~95%。与使用训练数据均值作为初始值相比，后者虽也加速预运行，但提升幅度小得多。
原文摘要 · Abstract (English)
We used precomputed steady states obtained by a spin-up for a global marine ecosystem model as training data to build a mapping from the small number of biogeochemical model parameters onto the three-dimensional converged steady annual cycle. The mapping was performed by a conditional variational autoencoder (CVAE) with mass correction. Applied for test data, we show that the prediction obtained by the CVAE already gives a reasonable good approximation of the steady states obtained by a regular spin-up. However, the predictions do not reach the same level of annual periodicity as those obtained in the original spin-up data. Thus, we took the predictions as initial values for a spin-up. We could show that the number of necessary iterations, corresponding to model years, to reach a prescribed stopping criterion in the spin-up could be significantly reduced compared to the use of the originally uniform, constant initial value. The amount of reduction depends on the applied stopping criterion, measuring the periodicity of the solution. The savings in needed iterations and, thus, computing time for the spin-up ranges from 50 to 95\%, depending on the stopping criterion for the spin-up. We compared these results with the use of the mean of the training data as an initial value. We found that this also accelerates the spin-up, but only by a much lower factor.
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