EVENTO
Novel Markov chain Monte Carlo methods applied to high-dimensional porous media problems
Tipo de evento: Exame de Qualificação
Markov chain Monte Carlo methods are widely used for uncertainty quantification in stochastic porous media problems. Despite advances in computational power, high stochastic dimensionality remains a significant challenge, particularly in reservoir simulation. Two key strategies have emerged to address this issue: dimension reduction and more efficient sampling techniques. Dimension reduction methods such as the KarhunenLoève Expansion (KLE) are commonly employed to generate permeability fields. While effective, these methods often lack flexibility. Recently, neural network-based approaches like Variational Autoencoders (VAE) have gained popularity for their ability to learn from diverse datasets, offering greater adaptability and robustness in representing uncertainty. From a sampling perspective, traditional proposal mechanisms often perform poorly in high-dimensional spaces. Differential Evolution MCMC (DE-McMC), introduced by Ter Braak, offers faster convergence by evolving multiple parallel chains. However, this comes at the cost of high computational demand. The DESk variant introduces a selection step in the direction to improve the original DE. This doctoral qualification work aims to integrate DE-based McMC methods with VAE-generated models to develop a family of techniques for high-dimensional uncertainty quantification in porous media flow problems. The text presents the student studies exploring the complexities involved in these approaches, along with a proposed set of directions for the remainder of the doctoral period.Evento HíbridoLocal: Auditório BLink da Transmissão:meet.google.com/ctz-uhro-ksf
Data Início: 30/06/2025 Hora: 10:00 Data Fim: 30/06/2025 Hora: 12:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Michel Antonio Tosin Caldas - - LNCC
Orientador: Marcio Rentes Borges - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Fabio Lima Custodio - Laboratório Nacional de Computação Científica - LNCC Helio Pedro Amaral Souto - Universidade do Estado do Rio de Janeiro - UERJ José Karam Filho - Laboratório Nacional de Computação Científica - LNCC Renato Simões Silva - Laboratório Nacional de Computação Científica - LNCC
