CBL - Campus del Baix Llobregat

Projecte ofert

Títol: Harnessing Data-Driven Mechanisms for Shape Engineering


Per assignar-vos el projecte us heu de dirigir al director/a perquè us l'assigni.


Director/a: RYZHAKOV, PAVEL

Departament: DECA

Títol: Harnessing Data-Driven Mechanisms for Shape Engineering

Data inici oferta: 26-04-2024     Data finalització oferta: 26-12-2024



Estudis d'assignació del projecte:
    GR ENG SIS TELECOMUN
    GR ENG SIST AEROESP
    GR ENG TELEMÀTICA
Tipus: Individual
 
Lloc de realització:
UPC
    Departament: DECA
 
Paraules clau:
Data science, neural networks, machine learning
 
Descripció del contingut i pla d'activitats:
In the realm of automated aerodynamics, the shaping of aircraft forms using operational settings is
deemed essential. This proposal aims to emulate aircraft design by focusing on basic geometric
configurations. Through predictive modeling, an understanding is sought regarding how shape features
are influenced by operational settings. By employing machine learning and data-driven techniques,
efforts will be made to study accurate shape prediction models with eventual applications in engineering
real-world aircraft scenarios.

Objectives and Tasks
1. Familiarize with pertinent literature on Machine Learning Data-Driven Models, including a
comparative analysis of predictive models like Radial Basis Function (RBF) and Neural Networks
(NN).
2. Understand the fundamentals of data preprocessing, correlation analysis, and clustering methods
to effectively analyze and interpret datasets, incorporating an exploration of OpenCV libraries for
image recognition.
3. Familiarize with libraries like TensorFlow and PyTorch, and explore alternative options for creating
predictive models.
4. Developing an initial framework to precisely predict shape features, tailored specifically for future
applications in aircraft engineering.
Responsibilities
This project will be supervised by Professor Pavel Ryzhakov, who will guide and oversee the progress of
the tasks.

Note: The proposed plan is subject to adjustments and refinements based on the availability of resources
and consultation with the supervisor. The TFG will be supervised by Prof. P. Ryzhakov and Dr. A. Aares de Parga.
 
Orientació a l'estudiant:
Programming skills, basic knowledge of machine learning
 
Requereix activitats hardware: No
 
Requereix activitats software: No
 
Horari d'atenció a estudiants per a l'assignació de projecte:

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