EnsembleSimulationen

Ensemble simulations are a technique used in various scientific fields to account for uncertainty in model predictions. Instead of running a single simulation with a fixed set of parameters, an ensemble simulation involves running multiple simulations, each with slightly different input conditions or model parameters. These variations are typically drawn from probability distributions that represent the known or estimated uncertainty in the real-world system being modeled.

The core idea behind ensemble simulations is to capture a range of possible future outcomes. By analyzing the collection of results from the ensemble members, scientists can gain insights into the most probable scenarios, the extent of uncertainty surrounding those scenarios, and the sensitivity of the model to different input parameters. This is particularly valuable in fields like weather forecasting, climate modeling, and financial risk assessment, where precise predictions are often impossible due to inherent complexity and incomplete knowledge of all influencing factors.

The output of an ensemble simulation is not a single prediction but rather a statistical distribution of outcomes. This allows for the calculation of probabilities for specific events, the identification of outlier scenarios, and the assessment of confidence in the overall model predictions. By understanding the spread of results, decision-makers can better prepare for a wider range of possibilities and make more robust plans.