LowFidelityModelle

LowFidelityModelle are simplified representations of a system, process, or product that emphasize basic structure and behavior while omitting detailed features, precision, and complex interactions. They are commonly used in early design, planning, and analysis to test concepts, compare alternatives, and communicate ideas among stakeholders.

Typical implementations include lumped-parameter or reduced-physics models in engineering, linear or piecewise-linear approximations in control theory, surrogate or heuristic models in data analysis, and rapid prototyping artifacts in product design or user interfaces (such as paper prototypes or wireframes). The common goal is to achieve fast, transparent, and interpretable results at the cost of numerical accuracy or fidelity to the real system.

Advantages of LowFidelityModelle include faster development cycles, lower cost, easier understanding, and the ability to explore a wide design space without requiring expensive data or simulations. They are particularly valuable in early-stage feasibility studies, requirement elicitation, and risk assessment.

Limitations include limited predictive power, potential oversimplification of nonlinear effects or couplings, and the risk of wrong conclusions if extrapolated beyond the intended scope. They are typically followed by validation and refinement using higher-fidelity models as project details mature.

LowFidelityModelle are part of a model hierarchy that supports iterative design and model-based decision making, complementing high-fidelity models, experiments, and real-world testing.