Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integrating Machine Learning and Multiscale Modeling: Perspectives,
  Challenges, and Opportunities in the Biological, Biomedical, and Behavioral
  Sciences   Fueled by breakthrough technology developments, the biological, biomedical,
and behavioral sciences are now collecting more data than ever before. There is
a critical need for time- and cost-efficient strategies to analyze and
interpret these data to advance human health. The recent rise of machine
learning as a powerful technique to integrate multimodality, multifidelity
data, and reveal correlations between intertwined phenomena presents a special
opportunity in this regard. However, classical machine learning techniques
often ignore the fundamental laws of physics and result in ill-posed problems
or non-physical solutions. Multiscale modeling is a successful strategy to
integrate multiscale, multiphysics data and uncover mechanisms that explain the
emergence of function. However, multiscale modeling alone often fails to
efficiently combine large data sets from different sources and different levels
of resolution. We show how machine learning and multiscale modeling can
complement each other to create robust predictive models that integrate the
underlying physics to manage ill-posed problems and explore massive design
spaces. We critically review the current literature, highlight applications and
opportunities, address open questions, and discuss potential challenges and
limitations in four overarching topical areas: ordinary differential equations,
partial differential equations, data-driven approaches, and theory-driven
approaches. Towards these goals, we leverage expertise in applied mathematics,
computer science, computational biology, biophysics, biomechanics, engineering
mechanics, experimentation, and medicine. Our multidisciplinary perspective
suggests that integrating machine learning and multiscale modeling can provide
new insights into disease mechanisms, help identify new targets and treatment
strategies, and inform decision making for the benefit of human health.

2
The categories of this paper are: math
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs