Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning the Physics of Transport Phenomena   We have developed a new data-driven paradigm for the rapid inference,
modeling and simulation of the physics of transport phenomena by deep learning.
Using conditional generative adversarial networks (cGAN), we train models for
the direct generation of solutions to steady state heat conduction and
incompressible fluid flow purely on observation without knowledge of the
underlying governing equations. Rather than using iterative numerical methods
to approximate the solution of the constitutive equations, cGANs learn to
directly generate the solutions to these phenomena, given arbitrary boundary
conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art
computational performance. The cGAN framework can be used to learn causal
models directly from experimental observations where the underlying physical
model is complex or unknown.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph