| 03-04-2024 |
Métodos iterativos para discretizaciones con elementos de frontera
El método de elementos de frontera (BEM) permite resolver ecuaciones escritas en dominios infinitos al reducir el sistema original a uno escrito solo en términos de lo que ocurre en su superficie. La desventaja de esto es que las matrices resultantes son densas, lo cual vuelve costoso invertir dichos sistemas. El método clásico para hacerlo es el GMRES, y este proyecto consiste en comparar este método con uno equivalente propuesto hace poco llamado AAR (Alternating Anderson Richardson). Si bien ambos métodos son teóricamente equivalentes, AAR tiene la ventaja de permitir reutilizar las iteraciones anteriores de manera más astuta que GMRES. Los códigos a usar están escritos en Python y Julia, por lo que es fundamental tener buen manejo de modos para comunicar distintas rutinas.
Keywords:
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/1 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 03-04-2024 |
Iterative methods for boundary element discretizations
Keywords:
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/1 available vacants |
Mentor(s): Open in the plataform |
| 17-10-2023 |
Numerical methods for ultrasound in biomedical engineering
Focused ultrasound can be used in a clinical context to non-invasively treat different types of cancer. The idea is to guide acoustic energy to a focal region in the human body and generate an ablation process. The challenge is to focus the energy in the tumor without overheating healthy tissue, organs, or bone. This project is part of a research line that designs numerical methods to simulate the propagation of acoustic waves in the human body. This requires the design of efficient algorithms and programming fast algorithms in Python.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |
| 17-10-2023 |
Machine learning to forecast heat waves
Heat waves occur every time more frequently and are more intense in Chile due to global warming. These extreme weather events have a severe impact on public health, the industry, and agronomy. Preparing for heat waves requires skillful prediction of when they occur and how intense they will be. This project involves the design of artificial intelligence algorithms based on machine learning to predict the likelihood of heat waves in Chile. The activities include collecting data from open sources, the design of mathematical algorithms, and programming in Python
Keywords:
machine learning
cambio climatico
Prerequisites:
IIC1103
Evaluation method: Nota 1-7, with 0/2 available vacants |
Mentor(s): Open in the plataform |