  Most modeling approaches lie in either of the two categories: physics-based
or data-driven. Recently, a third approach which is a combination of these
deterministic and statistical models is emerging for scientific applications.
To leverage these developments, our aim in this perspective paper is centered
around exploring numerous principle concepts to address the challenges of (i)
trustworthiness and generalizability in developing data-driven models to shed
light on understanding the fundamental trade-offs in their accuracy and
efficiency, and (ii) seamless integration of interface learning and
multifidelity coupling approaches that transfer and represent information
between different entities, particularly when different scales are governed by
different physics, each operating on a different level of abstraction.
Addressing these challenges could enable the revolution of digital twin
technologies for scientific and engineering applications.
