Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Physics in Mesh-reduced Space with Temporal Attention   Graph-based next-step prediction models have recently been very successful in
modeling complex high-dimensional physical systems on irregular meshes.
However, due to their short temporal attention span, these models suffer from
error accumulation and drift. In this paper, we propose a new method that
captures long-term dependencies through a transformer-style temporal attention
model. We introduce an encoder-decoder structure to summarize features and
create a compact mesh representation of the system state, to allow the temporal
model to operate on a low-dimensional mesh representations in a memory
efficient manner. Our method outperforms a competitive GNN baseline on several
complex fluid dynamics prediction tasks, from sonic shocks to vascular flow. We
demonstrate stable rollouts without the need for training noise and show
perfectly phase-stable predictions even for very long sequences. More broadly,
we believe our approach paves the way to bringing the benefits of
attention-based sequence models to solving high-dimensional complex physics
tasks.

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The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
17
The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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: plasm-ph
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The categories of this paper are: nucl-ex