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New trends on the systems approach to modeling SARS-CoV-2 pandemics in a globally connected planet
G Bertaglia, A Bondesan, D Burini, R Eftimie… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper presents a critical analysis of the literature and perspective research ideas for
modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic …
modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic …
Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
Uncertainty in data is certainly one of the main problems in epidemiology, as shown by the
recent COVID-19 pandemic. The need for efficient methods capable of quantifying …
recent COVID-19 pandemic. The need for efficient methods capable of quantifying …
Micro-macro stochastic Galerkin methods for nonlinear Fokker–Planck equations with random inputs
Nonlinear Fokker–Planck equations play a major role in modeling large systems of
interacting particles with a proved effectiveness in describing real world phenomena ranging …
interacting particles with a proved effectiveness in describing real world phenomena ranging …
[HTML][HTML] Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Uncertainties in an output of interest that depends on the solution of a complex system (eg,
of partial differential equations with random inputs) are often, if not nearly ubiquitously …
of partial differential equations with random inputs) are often, if not nearly ubiquitously …
Multi-fidelity and multi-level Monte Carlo methods for kinetic models of traffic flow
E Iacomini, L Pareschi - arxiv preprint arxiv:2501.15967, 2025 - arxiv.org
In traffic flow modeling, incorporating uncertainty is crucial for accurately capturing the
complexities of real-world scenarios. In this work we focus on kinetic models of traffic flow …
complexities of real-world scenarios. In this work we focus on kinetic models of traffic flow …
Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Uncertainties in an output of interest that depends on the solution of a complex system (eg,
of partial differential equations with random inputs) are often, if not nearly ubiquitously …
of partial differential equations with random inputs) are often, if not nearly ubiquitously …
Multigroup-like MC resolution of generalised Polynomial Chaos reduced models of the uncertain linear Boltzmann equation (+ discussion on hybrid intrusive/non …
G Poëtte - Journal of Computational Physics, 2023 - Elsevier
In this paper, we are interested in propagating uncertainties through the linear Boltzmann
equation. Such model is intensively used in neutronics, photonics, socio-economics …
equation. Such model is intensively used in neutronics, photonics, socio-economics …