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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Lasso Monte Carlo, a variation on multi fidelity methods for high-dimensional uncertainty quantification.

Arnau Albà^1,2, Romana Boiger¹, Dimitri Rochman¹

¹Paul Scherrer Institut, Villigen, Switzerland.

Journal of Applied Statistics

|December 4, 2025

Summary

Lasso Monte Carlo (LMC) offers efficient uncertainty quantification (UQ) for high-dimensional problems. This new method reduces computational costs significantly compared to traditional Monte Carlo methods.

Keywords:

62-08 62J07 62J10 62P35 Multifidelity Monte Carlo curse of dimensionality lasso uncertainty quantification

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Area of Science:

Computational Science and Engineering
Statistical Modeling

Background:

Uncertainty quantification (UQ) is crucial across science and engineering.
Common UQ methods include Monte Carlo (slow convergence) and surrogate modeling (curse of dimensionality for high-dimensional problems).

Purpose of the Study:

To introduce Lasso Monte Carlo (LMC), a novel technique for efficient UQ in high-dimensional settings.
To reduce the computational cost of UQ while maintaining accuracy.

Main Methods:

Combines a Lasso surrogate model with multifidelity Monte Carlo techniques.
Develops mathematical guarantees for the unbiasedness of the LMC method.

Main Results:

LMC demonstrates higher accuracy than simple Monte Carlo and other multifidelity methods in benchmark tests.
Achieves computational cost reductions of over 5x compared to simple Monte Carlo.
Validated on toy problems and a nuclear engineering UQ application.

Conclusions:

Lasso Monte Carlo (LMC) provides a computationally efficient and accurate approach for UQ in high-dimensional problems.
LMC overcomes the limitations of traditional methods, making UQ more feasible for complex applications.