Hyper Space Exploration - A Multicriterial Quantitative Trade-Off Analysis for System Design in Complex Environment

TL;DR

Hyper Space Exploration (HSE) is a new methodology combining virtual prototyping and statistical learning to identify optimal system designs and provide proof-of-concept in complex, uncertain environments.

Contribution

The paper introduces Hyper Space Exploration, a novel approach integrating virtual prototyping and design of virtual experiments for systematic trade-off analysis in complex system design.

Findings

HSE effectively identifies Pareto-optimal solutions.

HSE provides early feedback through virtual prototyping.

HSE has demonstrated successful applications in complex environments.

Abstract

Successful engineering requires environmentally adapted procedural and architectural approaches. While dealing with complicated issues has become an engineering standard mastering uncertainties in complex environment is still a major issue. Global trends, such as an increasing rate of disruptive technology changes or merging of technology fields, however, enforce the importance of this complex habitat. Missing experience in unknown technological territory faces engineers with two questions of paramount importance: 1) How can the best architectural solution within the space of potential alternatives be identified? 2) How can a proof-of-concept for considered solutions prior implementation be provided? Mastering lack of knowledge related risks and uncertainties states one of the most prominent tasks in according projects. The paper presents a novel methodology of system design in complex environment called Hyper Space Exploration (HSE). The HSE approach combines methods of virtual prototyping with those of design of virtual experiments based studies for statistical learning. Virtual prototyping allows an early feedback on system behavior with a proof-of-concept prior implementation. Statistical learning enables system architects to systematically build up the space of potential solution alternatives, model the effects of design and use-case variables on target indicators in complex territory, quantify target indicator trade-offs and finally identify Pareto-optimal system solutions. The first part of the paper characterizes engineering challenges in complex environment. Section two presents the HSE methodology with its two major constituents work flow and tool chain. Section three outlines first successful HSE applications that already proved its capabilities and universality. Final section four gives an outlook to further HSE applications as well as methodological future HSE extensions.