Banca de DEFESA: SUELEN REGINA DA SILVA LIMA FERRAZ

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : SUELEN REGINA DA SILVA LIMA FERRAZ
DATE: 28/02/2025
TIME: 14:00
LOCAL: VIDEOCONFERÊNCIA
TITLE:

APRENDIZADO DE MÁQUINA COMO FERRAMENTA DE RESOLUÇÃO PARA EQUAÇÕES DIFERENCIAIS ESTOCÁSTICAS

 

KEY WORDS:

Stochastic Differential Equations,Neural Network,Wiener Process,Time Series

PAGES: 49
BIG AREA: Ciências Exatas e da Terra
AREA: Probabilidade e Estatística
SUBÁREA: Estatística
SUMMARY:

Using neural networks to solve differential equations is an advantageous approach for analyzing continuous-time series due to their ability to handle the inherent nonlinearities and complexities of time series data. This approach enables the model to learn and adapt to the underlying properties of the system, enhancing flexibility and efficiency in the analysis and prediction of complex phenomena. In this work, we propose an application of the Euler-Maruyama method using three neural network architectures: Extreme Learning Machines (ELMs), Multi-Layer Perceptrons (MLPs), and Recurrent Neural Networks (RNNs). These networks are employed to solve a set of stochastic differential equations characterized by the Wiener process. The Euler-Maruyama scheme is utilized to discretize the stochastic equations, transforming them into a suitable form for neural network training. Empirical results demonstrate the effectiveness of this approach in accurately and efficiently solving stochastic differential equations.

 

COMMITTEE MEMBERS:
Externo ao Programa - 4479014 - ANTONIO DE PADUA SANTOS - UFRPEInterno - JADER DA SILVA JALE
Externo à Instituição - PAULO RENATO ALVES FIRMINO - UFCA
Presidente - TIAGO ALESSANDRO ESPINOLA FERREIRA