resimulation

Resimulation is the practice of running a model or simulation multiple times to examine outcomes under revised conditions, input data, or assumptions. It is used across science and engineering to test robustness, explore alternative scenarios, and update predictions as new information becomes available. Resimulation can produce an ensemble of results or refine a model’s trajectory by iterating the simulation process.

Common applications include calibration and validation, sensitivity analysis, and uncertainty quantification. In data assimilation, resimulation is combined with observations to update the model state and improve forecasts. In ensemble forecasting, many resimulations are executed with varied initial conditions or parameters to reflect uncertainty. In engineering and physics, resimulation may involve re-solving governing equations under refined constraints or numerical methods to ensure stability and physical consistency.

Domains such as climate science, epidemiology, finance, and manufacturing use resimulation for scenario planning, risk assessment, and decision support. Methodologically, resimulation involves selecting inputs or random seeds, running the simulation, comparing outputs to data or objectives, and iterating to adjust parameters or models. It often relies on automation, parallel computation, and statistical tools for analysis.

Limitations include computational cost, potential biases from model structure, and data quality. Reproducibility must be maintained by documenting inputs, random seeds, and software versions. Resimulation is closely related to data assimilation, ensemble methods, calibration, and sensitivity analysis.