A03. Machine learning approaches for surrogate modeling

Poster Title	Machine learning approaches for surrogate modeling in the E3SM land model
First Author	Daniel Ricciuto
Topic	BGC, land/energy model development, software tools
Affiliation	BGC, OSCM-SciDAC
Link to document	...

Title

Machine learning approaches for surrogate modeling in the E3SM land model

Authors

Daniel Ricciuto, Dan Lu, Khachik Sargsyan, Vishagan Ratnaswamy, Cosmin Safta (Unlicensed)

Abstract

There are a variety of different methods in machine learning that can be applied to create surrogate models. Traditional feed-forward neural networks or a multilayer perceptron (MLP) can be used to build approximations to quantities of interest (QoI) for complex physical models, for example, carbon fluxes in the E3SM land model. A single model output variable (e.g. the gross primary productivity GPP) is spatially gridded and therefore contains a large number of QoIs for a surrogate model to reproduce. Here we demonstrate this high-dimensional GPP output can be accurately represented with a small number of singular values when singular value decomposition (SVD) is applied. An accurate surrogate model can then be trained using a MLP with a relatively small ensemble. Temporal variations in model outputs present additional challenges for creating accurate surrogate models. Thus, the use of a recurrent neural network (RNN) is also suited for the land model. Using a vanilla RNN comes with its own set of issues such as exploding and vanishing gradients; however, those issues can be mitigated with gradient clipping or commonly gates. One common gated method is long short-term memory (LSTM). While the gated-RNN can handle temporal data, it is typically done in a sequential fashion, i.e. it ignores the connected (hierarchical) nature of the QOIs. To make a more physics-based model, we employ a hierarchical NN, specifically a Tree-LSTM that incorporates the hierarchical nature of the land model. We compare how well the Tree-LSTM RNN predicted the QOIs of the land model in one representative grid cell, namely for carbon cycle variables compared with LSTM-RNN and MLP. We find that the Tree-LSTM outperforms MLP and LSTM-RNN, confirming the intuition that physics-based neural network architecture improves the predictive accuracy compared to vanilla methods.