  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.
