This paper presents a novel approach for generating parametric models based on implicit functions to enable rapid and robust Design Exploration (DE) and Multidisciplinary Design Optimization (MDO) in conceptual and preliminary design, where thousands to tens of thousands of design variants must be generated and evaluated. Most existing CAD systems rely on Boundary Representation (BRep) models, which are highly susceptible to computational failures and often require significant manual intervention. A second major impediment to effective DE and MDO is automated integration of downstream analysis tools (e.g., aerodynamics/CFD, structures, and electromagnetics) across large numbers of design variants. Current workflows typically require BRep models to be converted into simulation-specific conformal meshes, a process that can consume more than 70% of the total design effort and remains fragile in the presence of substantial geometric or topological changes. By leveraging implicit modeling evaluated on GPU architectures, in combination with simulation techniques that eliminate the need for conformal meshing and AI-trained surrogate models, the integrated approach presented here dramatically reduces human effort and accelerates computational workflows, compressing comprehensive design studies from months to weeks or even days.