Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Material: Learning Elastic Constitutive Material and Damping
  Models from Sparse Data   The accuracy and fidelity of deformation simulations are highly dependent
upon the underlying constitutive material model. Commonly used linear or
nonlinear constitutive material models only cover a tiny part of possible
material behavior. In this work we propose a unified framework for modeling
deformable material. The key idea is to use a neural network to correct a
nominal model of the elastic and damping properties of the object. The neural
network encapsulates a complex function that is hard to explicitly model. It
injects force corrections that help the forward simulation to more accurately
predict the true behavior of a given soft object, which includes non-linear
elastic forces and damping. Attempting to satisfy the requirement from real
material interference and animation design scenarios, we learn material models
from examples of dynamic behavior of a deformable object's surface. The
challenge is that such data is sparse as it is consistently given only on part
of the surface. Sparse reduced space-time optimization is employed to gradually
generate increasingly accurate training data, which further refines and
enhances the neural network. We evaluate our choice of network architecture and
show evidence that the modest amount of training data we use is suitable for
the problem tackled. Our method is demonstrated with a set of synthetic
examples.

33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat