Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring the potential of transfer learning for metamodels of
  heterogeneous material deformation   From the nano-scale to the macro-scale, biological tissue is spatially
heterogeneous. Even when tissue behavior is well understood, the exact subject
specific spatial distribution of material properties is often unknown. And,
when developing computational models of biological tissue, it is usually
prohibitively computationally expensive to simulate every plausible spatial
distribution of material properties for each problem of interest. Therefore,
one of the major challenges in developing accurate computational models of
biological tissue is capturing the potential effects of this spatial
heterogeneity. Recently, machine learning based metamodels have gained
popularity as a computationally tractable way to overcome this problem because
they can make predictions based on a limited number of direct simulation runs.
These metamodels are promising, but they often still require a high number of
direct simulations to achieve an acceptable performance. Here we show that
transfer learning, a strategy where knowledge gained while solving one problem
is transferred to solving a different but related problem, can help overcome
this limitation. Critically, transfer learning can be used to leverage both
low-fidelity simulation data and simulation data that is the outcome of solving
a different but related mechanical problem. In this paper, we extend Mechanical
MNIST, our open source benchmark dataset of heterogeneous material undergoing
large deformation, to include a selection of low-fidelity simulation results
that require 2-4 orders of magnitude less CPU time to run. Then, we show that
transferring the knowledge stored in metamodels trained on these low-fidelity
simulation results can vastly improve the performance of metamodels used to
predict the results of high-fidelity simulations.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math