Seamless Digital Engineering: A Grand Challenge Driven by Needs
Digital Engineering currently relies on costly and often bespoke integration of disparate software products to assemble the authoritative source of truth of the system-of-interest. Tools not originally designed to work together become an acknowledged system-of-systems, with their own separate feature roadmaps, deprecation, and support timelines. The resulting brittleness and conglomeration of disparate interfaces in the Digital Engineering Ecosystem of an organization drains resources and impairs efficiency and efficacy. If Model-Based Systems Engineering were applied to this problem, a complete system architecture model would be defined, and a purpose-built computing system-of-systems would be constructed to satisfy stakeholder needs. We have decades of research in computer science, cybersecurity, software and systems engineering, and human-computer interaction from which to draw that informs the design of a Seamless Digital Engineering tooling system, but it would require starting from a clean slate while carefully adopting existing standards. In this paper, this problem space and solution space are characterized, defining and identifying Seamless Digital Engineering as a grand challenge in Digital Engineering research.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
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 a combination of these deterministic and statistical models is emerging for scientific appli…
PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing
A comprehensive understanding of heat transport is essential for optimizing various mechanical and engineering applications, including 3D printing. Recent advances in machine learning, combined with physics-based models,…
Transfer LearningGraph LearningLearning with Physical Constraints
This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocit…
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification
The increasing significance of digital twin technology across engineering and industrial domains, such as aerospace, infrastructure, and automotive, is undeniable. However, the lack of detailed application-specific infor…
Uncertainty QuantificationThe Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design
The grand challenge of protein engineering is the development of computational models that can characterize and generate protein sequences for any arbitrary function. However, progress today is limited by lack of 1) benc…
Benchmarking