Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computationally-Efficient Climate Predictions using Multi-Fidelity
  Surrogate Modelling   Accurately modelling the Earth's climate has widespread applications ranging
from forecasting local weather to understanding global climate change.
Low-fidelity simulations of climate phenomena are readily available, but
high-fidelity simulations are expensive to obtain. We therefore investigate the
potential of Gaussian process-based multi-fidelity surrogate modelling as a way
to produce high-fidelity climate predictions at low cost. Specifically, our
model combines the predictions of a low-fidelity Global Climate Model (GCM) and
those of a high-fidelity Regional Climate Model (RCM) to produce high-fidelity
temperature predictions for a mountainous region on the coastline of Peru. We
are able to produce high-fidelity temperature predictions at significantly
lower computational cost compared to the high-fidelity model alone: our
predictions have an average error of $15.62^\circ\text{C}^2$ yet our approach
only evaluates the high-fidelity model on 6% of the region of interest.

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The categories of this paper are: chem-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math