Modellfidelity

Modellfidelity refers to the degree to which a model reproduces the essential characteristics of the real system or process it aims to represent. It encompasses structural, parametric, and dynamic aspects, and is a key consideration in modeling, simulation, and decision support. Higher fidelity means closer alignment with reality but often comes with increased data requirements, complexity, and computation time.

Fidelity can be described along a spectrum from low-fidelity models—simplified, analytic, or rule-based—to high-fidelity models, which are detailed, physics-based, or data-driven. The choice depends on purpose, time constraints, and available data.

Assessing fidelity involves validation and verification. Validation compares model outputs with observed data; verification checks that the model correctly implements the intended design. Additional methods include calibration, sensitivity analysis, uncertainty quantification, and benchmarking.

Factors affecting fidelity include input data quality, the appropriateness of assumptions, level of detail, and resource limits. Trade-offs are common: higher fidelity improves accuracy but raises cost, complexity, and potential overfitting; lower fidelity supports quick insight or real-time use.

Applications span product design, digital twins, training simulations, and policy modeling. Examples include high-fidelity computational fluid dynamics for aerodynamics, reduced-order models for real-time optimization, and calibrated epidemiological models.

See also: model validation, model calibration, digital twin.