Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution
  Simulation of Blood Vessel Flows   In this paper, a novel deep learning framework is proposed for temporal
super-resolution simulation of blood vessel flows, in which a
high-temporal-resolution time-varying blood vessel flow simulation is generated
from a low-temporal-resolution flow simulation result. In our framework,
point-cloud is used to represent the complex blood vessel model,
resistance-time aided PointNet model is proposed for extracting the time-space
features of the time-varying flow field, and finally we can reconstruct the
high-accuracy and high-resolution flow field through the Decoder module. In
particular, the amplitude loss and the orientation loss of the velocity are
proposed from the vector characteristics of the velocity. And the combination
of these two metrics constitutes the final loss function for network training.
Several examples are given to illustrate the effective and efficiency of the
proposed framework for temporal super-resolution simulation of blood vessel
flows.

4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg