3 papers
math.NA2026
Control variates with neural surrogates for uncertainty quantification in kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
Efficient uncertainty quantification for kinetic equations with random inputs is challenging because it requires repeated simulations of high-dimensional models, such as the Boltzm…
cs.LG2025
Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) ess…
math.NA2025
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
The high dimensionality of kinetic equations with stochastic parameters poses major computational challenges for uncertainty quantification (UQ). Traditional Monte Carlo (MC) sampl…