van der Merwe Rudolph, Leen Todd K, Lu Zhengdong, Frolov Sergey, Baptista Antonio M
Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health and Science University, Portland, OR 97006, USA.
Neural Netw. 2007 May;20(4):462-78. doi: 10.1016/j.neunet.2007.04.023. Epub 2007 May 3.
We present neural network surrogates that provide extremely fast and accurate emulation of a large-scale circulation model for the coupled Columbia River, its estuary and near ocean regions. The circulation model has O(10(7)) degrees of freedom, is highly nonlinear and is driven by ocean, atmospheric and river influences at its boundaries. The surrogates provide accurate emulation of the full circulation code and run over 1000 times faster. Such fast dynamic surrogates will enable significant advances in ensemble forecasts in oceanography and weather.
我们展示了神经网络替代模型，它能对哥伦比亚河及其河口和近海区域的大规模环流模型进行极其快速且准确的模拟。该环流模型具有O(10(7))自由度，高度非线性，且在其边界受海洋、大气和河流影响驱动。这些替代模型能对完整的环流代码进行准确模拟，运行速度快1000倍以上。这种快速的动态替代模型将推动海洋学和气象学集合预报取得重大进展。
