Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Quantification in Neural Differential Equations   Uncertainty quantification (UQ) helps to make trustworthy predictions based
on collected observations and uncertain domain knowledge. With increased usage
of deep learning in various applications, the need for efficient UQ methods
that can make deep models more reliable has increased as well. Among
applications that can benefit from effective handling of uncertainty are the
deep learning based differential equation (DE) solvers. We adapt several
state-of-the-art UQ methods to get the predictive uncertainty for DE solutions
and show the results on four different DE types.

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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th