There are several approaches to model uncertainty in catalysis:
Statistical Methods: Statistical techniques such as Monte Carlo simulations and Bayesian inference are commonly used to quantify uncertainty. These methods allow for the incorporation of variability and the generation of probability distributions for key parameters. Machine Learning: Machine learning algorithms can be trained on experimental data to predict the performance of catalysts under different conditions, accounting for uncertainties in the input data. Sensitivity Analysis: This involves systematically varying input parameters to assess their impact on the output, helping to identify which parameters have the most significant effect on catalytic performance.
