Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations
  with Machine Learning   Failure in brittle materials under dynamic loading conditions is a result of
the propagation and coalescence of microcracks. Simulating this mechanism at
the continuum level is computationally expensive or, in some cases,
intractable. The computational cost is due to the need for highly resolved
computational meshes required to capture complex crack growth behavior, such as
branching, turning, etc. Typically, continuum-scale models that account for
brittle damage evolution homogenize the crack network in some way, which
reduces the overall computational cost, but can also neglect key physics of the
subgrid crack growth behavior, sacrificing accuracy for efficiency. We have
developed an approach using machine learning that overcomes the current
inability to represent micro-scale physics at the macro-scale. Our approach
leverages damage and stress data from a high-fidelity model that explicitly
resolves microcrack behavior to build an inexpensive machine learning emulator,
which runs in seconds as opposed to the high-fidelity model, which takes hours.
Once trained, the machine learning emulator is used to predict the evolution of
crack length statistics. A continuum-scale constitutive model is then informed
with these crack statistics, speeding up the workflow by four orders of
magnitude. Both the machine learning model and the continuum-scale model are
validated against a high-fidelity model and experimental data, respectively,
showing excellent agreement. There are two key findings. The first is that we
can reduce the dimensionality of the problem, establishing that the machine
learning emulator only needs the length of the longest crack and one of the
maximum stress components to capture the necessary physics. Another compelling
finding is that the emulator can be trained in one experimental setting and
transferred successfully to predict behavior in a different setting.

32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs