Monte Carlo Simulation Results for Particle Travel Time Uncertainty

Numerical results from four Monte Carlo simulation methods applied to uncertainty quantification for particle travel times in heterogeneous porous media. The data was generated by David Crevillen-Garcia in 2020 to compare the computational cost and accuracy of Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo, and multilevel quasi-Monte Carlo methods. It supports analysis of mean square error and computational efficiency for three different error tolerances.

Use Cases

• Compare the computational cost results for Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo, and multilevel quasi-Monte Carlo methods across three specified error tolerances.
• Analyze the mean square error metrics to quantify the accuracy differences between the four simulation methodologies.
• Validate uncertainty quantification models for particle travel times using the provided numerical results from simulations with log-Gaussian random field inputs.

Strengths

• Data originates from a published academic study focused on rigorous methodology comparison.
• Includes results for four distinct simulation methods (Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo, multilevel quasi-Monte Carlo).
• Quantifies accuracy using mean square error and compares computational cost for three different numerical tolerances.

Limitations

• The underlying sample size, row count, and specific data structure are unknown.
• Data is from a single model problem, limiting generalizability to other physical configurations.
• Last updated in 2020, so newer methodological advances are not reflected.

Provenance

Source

Dryad digital repository.

Collection Method

Generated from numerical simulations applying four Monte Carlo methods to a model problem with hydraulic conductivity as a log-Gaussian random field.

Freshness

Last updated on 2020-06-24.