Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parallel Physics-Informed Neural Networks with Bidirectional Balance   As an emerging technology in deep learning, physics-informed neural networks
(PINNs) have been widely used to solve various partial differential equations
(PDEs) in engineering. However, PDEs based on practical considerations contain
multiple physical quantities and complex initial boundary conditions, thus
PINNs often returns incorrect results. Here we take heat transfer problem in
multilayer fabrics as a typical example. It is coupled by multiple temperature
fields with strong correlation, and the values of variables are extremely
unbalanced among different dimensions. We clarify the potential difficulties of
solving such problems by classic PINNs, and propose a parallel physics-informed
neural networks with bidirectional balance. In detail, our parallel solving
framework synchronously fits coupled equations through several multilayer
perceptions. Moreover, we design two modules to balance forward process of data
and back-propagation process of loss gradient. This bidirectional balance not
only enables the whole network to converge stably, but also helps to fully
learn various physical conditions in PDEs. We provide a series of ablation
experiments to verify the effectiveness of the proposed methods. The results
show that our approach makes the PINNs unsolvable problem solvable, and
achieves excellent solving accuracy.

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The categories of this paper are: eess
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin