Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning for the rare-event rational design of 3D printed
  multi-material mechanical metamaterials   Emerging multi-material 3D printing techniques have paved the way for the
rational design of metamaterials with not only complex geometries but also
arbitrary distributions of multiple materials within those geometries. Varying
the spatial distribution of multiple materials gives rise to many interesting
and potentially unique combinations of anisotropic elastic properties. While
the availability of a design approach to cover a large portion of all possible
combinations of elastic properties is interesting in itself, it is even more
important to find the extremely rare designs that lead to highly unusual
combinations of material properties (e.g., double-auxeticity and high elastic
moduli). Here, we used a random distribution of a hard phase and a soft phase
within a regular lattice to study the resulting anisotropic mechanical
properties of the network in general and the abovementioned rare designs in
particular. The primary challenge to take up concerns the huge number of design
parameters and the extreme rarity of such designs. We, therefore, used
computational models and deep learning algorithms to create a mapping from the
space of design parameters to the space of mechanical properties, thereby (i)
reducing the computational time required for evaluating each designand (ii)
making the process of evaluating the different designs highly parallelizable.
Furthermore, we selected ten designs to be fabricated using polyjet
multi-material 3D printing techniques, mechanically tested them, and
characterized their behavior using digital image correlation (DIC, 3 designs)
to validate the accuracy of our computational models. The results of our
simulations show that deep learning-based algorithms can accurately predict the
mechanical properties of the different designs, which match the various
deformation mechanisms observed in the experiments.

12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ