Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

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