Simulation Error

+Simulation Error refers to the divergence between a simulated outcome and the real-world phenomenon or ideal model it aims to represent. These discrepancies can stem from approximations, [numerical error](/wiki/numerical_error), or fundamental limits in [model accuracy](/wiki/model_accuracy), affecting the trustworthiness of predictive analyses. Understanding and quantifying these errors is crucial for interpreting simulation results and making informed decisions based on them.

+Simulation errors can arise from various sources, often categorized based on their origin:

+- **Model Imperfections:** The underlying mathematical or conceptual model may not perfectly capture the complexity of the real system. This can involve making simplifying assumptions, omitting relevant physical processes, or using incorrect constitutive relations. Such simplifications are often necessary for computational feasibility but introduce [model bias](/wiki/model_bias) or [structural error](/wiki/structural_error). The initial [model design](/wiki/model_design) directly impacts the potential for this type of error.

+- **Numerical Approximations:** Most simulations rely on numerical methods to solve complex equations. These methods often involve discretizing continuous systems (e.g., using [finite element](/wiki/finite_element) or [finite difference](/wiki/finite_difference) methods), truncating infinite series, or approximating integrals. Each of these steps introduces [truncation error](/wiki/truncation_error) and [discretization error](/wiki/discretization_error), which are dependent on factors like step size, mesh resolution, or order of approximation.

+- **Computational Limitations:** Even theoretically perfect numerical methods can suffer from [floating-point error](/wiki/floating_point_error) due to the finite precision of computer arithmetic. This can lead to [round-off error](/wiki/round_off_error) and, in sensitive systems, can accumulate and lead to [numerical instability](/wiki/numerical_stability). The choice of [algorithm](/wiki/algorithm) and its implementation critically impacts these errors.

+**Simulation Error**

+Simulation Error refers to the divergence between a simulated outcome and the real-world phenomenon or ideal model it aims to represent. These discrepancies can stem from approximations, [numerical error](/wiki/numerical_error), or fundamental limits in [model accuracy](/wiki/model_accuracy), affecting the trustworthiness of predictive analyses.

+## See also

+- [Model Validation](/wiki/model_validation)

+- [Data Fidelity](/wiki/data_fidelity)