Doctoral School

Reading date: 07/05/2024

• DÍEZ MÉRIDA, JAIME: Probing Magic-Angle Twisted Bilayer Graphene with Gate Defined homo-Junctions
  
  Author: DÍEZ MÉRIDA, JAIME
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 09/04/2024
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: EFETOV, DMITRI | LEWENSTEIN, MACIEJ
  Committee:
       PRESIDENT: WEITZ, THOMAS
       SECRETARI: RUBIO VERDÚ, CARMEN
       VOCAL: RIBEIRO PALAU, REBECA LISSETTE
  Thesis abstract: In 2018, following a theoretical prediction from 2011, it was found that stacking two layers of graphene with a relative twist angle of 1.1° between them leads to multiple new properties. At this so-called magic angle, the electronic band structure of the material reconstructs, creating a narrow flat band at the Fermi level. The formation of a flat band enhances electron-electron interactions, resulting in the emergence of states of matter not present in the original graphene layers, including correlated insulators, superconductivity, ferromagnetism and non-trivial topological states. The understanding of the origin of these correlated states could help unravel the physics of highly correlated flat band systems which could potentially provide key technological developments. The main objective of this thesis is to study magic-angle twisted bilayer graphene (MATBG) by creating monolithic gate-defined Josephson junctions. By exploiting the rich phase space of the material, we can create a Josephson junction by independently tuning the superconductor and the weak link state. Studying the Josephson effect is a first step towards understanding fundamental properties of a superconductor, such as its order parameter. First, we have optimized the fabrication of these gate-defined junctions made of all van der Waals materials. We have made double-graphite-gated hBN encapsulated MATBG devices where the top gate is split into two parts via nanolithography techniques. This configuration allows to independently control the three regions of the Josephson junction (superconductor, weak-link and superconductor). Then, we have studied the gate-defined Josephson junctions via low-temperature transport measurements. After demonstrating the Josephson effect in the fabricated devices, we focus on the behavior of one of these junctions in great detail. In particular, we have observed an unconventional behavior when the weak link of the junction is set close to the correlated insulator at half-filling of the hole-side flatband. We have observed a phase shifted Fraunhofer pattern with a pronounced magnetic hysteresis, characteristic of magnetic Josephson junctions. To understand the origin of the signals, we have performed a critical current distribution Fourier analysis as well as a tight binding calculation of a MATBG Josephson junction. Our theoretical calculations with a valley polarized state as the weak link can explain the key signatures observed in the experiment. Lastly, the combination of magnetization and its current-induced magnetization switching has allowed us to realize a programmable zero-field superconducting diode.Finally, we have shown the flexibility of these devices by studying a MATBG p-n junction under light illumination. We have studied the relaxation dynamics of hot electrons using time and frequency-resolved photovoltage measurements. The measurements have revealed an ultrafast cooling in MATBG compared to Bernal-bilayer from room temperature down to 5 K. The enhanced cooling in MATBG can be explained by the presence of the moiré pattern and corresponding mini-Brillouin zone. In summary, we have demonstrated that by integrating various MATBG states within a single device, we can gain a deeper insight into the system's properties and can engineer innovative, complex hybrid structures, such as magnetic Josephson junctions and superconducting diodes.
  

• NÚÑEZ CORBACHO, MARC: Aerodynamic shape optimization under uncertainties using embedded methods and adjoint techniques
  
  Author: NÚÑEZ CORBACHO, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Normal
  Deposit date: 09/04/2024
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ROSSI BERNECOLI, RICCARDO | BAIGES AZNAR, JOAN
  Committee:
       PRESIDENT: LEHMKUHL BARBA, ORIOL
       SECRETARI: MARTINEZ FRUTOS, JESUS
       VOCAL: RICCHIUTO, MARIO
  Thesis abstract: This thesis develops a framework to perform shape optimization under uncertainties for a body under the action of aerodynamic forces. The solution of the flow is performed with finite elements using the full potential equation with an embedded approach, where the object of study is defined implicitly with a level set function. The optimization problem is solved by combining different software packages to perform the solution of the flow, advance in the optimization loop and perform uncertainty quantification. The first contribution of the thesis is the development of a full embedded approach for the solution of the full potential equation. Due to the inviscid hypothesis of potential solvers, these require the definition of a gap in the computational mesh in order to generate lift, known as the wake. Based on previous works where the wake is defined implicitly with an embedded approach, this work also considers the geometry as an embedded body. Mesh refinement and numerical terms are employed to improve the definition of the geometry in the mesh and ensure the definition of the Kutta condition. The solver is validated for two and three dimensions for subsonic and transonic flows with different reference data. Another contribution of the thesis is the development of the adjoint analysis for the subsonic full potential equation with embedded geometries in two dimensions. Each coordinate of the object of study is considered a design parameter in the adjoint analysis, where the effect of the level set function is considered. The sensitivities of the objective function with respect to the design parameters are validated by comparing them to the sensitivities obtained by using a finite differences approach. A shape optimization problem where the lift coefficient is maximized with geometrical constraints is solved as an example of application of the adjoint sensitivities. The embedded shape optimization problem is extended to consider uncertainties in the inlet condition. The optimization problem is reformulated by choosing a risk measure, the Conditional Value-at-risk, which is minimized. The adjoint sensitivities are adapted for the stochastic case, considering the selected risk measure. The estimation of the risk measure is performed thanks to an external uncertainty quantification library, by applying a novel approach which uses Monte Carlo methods to estimate the Conditional Value-at-risk. The stochastic case is solved in a distributed environment, where each optimization step deploys a Monte Carlo hierarchy to estimate the objective function and its gradients.
  

• ZHANG, ZHIHUI: Linking architecture and emotions: sensory dynamics and methodological innovations
  
  Author: ZHANG, ZHIHUI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARCHITECTURAL, CIVIL AND URBAN HERITAGE AND REFURBISHMENT OF EXISTING BUILDINGS
  Department: (RA)
  Mode: Article-based thesis
  Deposit date: 25/03/2024
  Reading date: 07/05/2024
  Reading time: 11:30
  Reading place: Sala de Graus ETSAB. Planta Baixa (E.T.S. Arquitectura de Barcelona)
  Thesis director: FORT MIR, JOSEP MARIA | GIMÉNEZ MATEU, LUIS
  Committee:
       PRESIDENT: FONSECA ESCUDERO, DAVID
       SECRETARI: NAVARRO DELGADO, ISIDRO
       VOCAL: VENTURA RODÀ, ORIOL
  Thesis abstract: This study delves deeply into the complex relationship between the field of architecture and human emotions, aiming to fill a significant gap in existing research. It extensively explores the profound impact of architectural design elements, such as lighting, colour schemes, and the integration of natural landscapes, on emotional responses. This research goes beyond traditional focuses on aesthetics and sustainability, striving to innovate methods for assessing the emotional impact of architectural spaces.In this study, we adopted a technological pathway from the laboratory to virtual reality, and finally to AI, combining theoretical analysis with practical experiments and case studies. The main research includes examining the effects of lighting and spatial dimension variations on people's emotions, as well as the application of facial emotion recognition technology in virtual reality architectural environments, exploring AI's perceptual capabilities as a tool in architectural design. These studies aim to narrow the gap between theoretical research and practical application, providing new perspectives and empirical data for the field of architectural design.The study concludes with a reflection on the methodologies used and their broader implications for architectural design practice. It offers specific strategies for architects and designers, aimed at creating spaces that resonate emotionally and add substantial value to human experiences. By prioritizing emotional factors in the design process, this research seeks to enhance overall quality of life and promote well-being in thoughtfully designed architectural spaces.
  

Reading date: 08/05/2024

• ALONSO ALONSO, JESUS: Dynamic Terrain Modeling
  
  Author: ALONSO ALONSO, JESUS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTING
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 18/03/2024
  Reading date: 08/05/2024
  Reading time: 12:00
  Reading place: Sala de Teleensenyament del 'ETSETB, edifici B3, Barcelona Diagonal NORD
  Thesis director: JOAN ARINYO, ROBERT
  Committee:
       PRESIDENT: CHOVER, MIGUEL
       SECRETARI: ARGUDO MEDRANO, OSCAR
       VOCAL: BOSCH GELI, CARLES
  Thesis abstract: This work explores terrain modelling techniques that provide a comprehensive experience in terms of graphical representation, physics interaction and dynamic updates in real-time. In particular, our focus revolves around the creation of a system able to: 1) capture any possible feature we find on terrains, 2) maintain an accurate level of detail, 3) offer rendering and navigation in real-time, 4) include the option of performing dynamic updates in real-time, and 5) support physical interactions of entities also in real-time.Once previous models from the literature are reviewed, two models are proposed that take a digital elevation model as the base structure. The former follows a strategy in which we mimic the geotectonic events we find in nature. The latter uses a sculpting approach with convex polyhedra as a carving tool. To this end, several works are presented.While the first option introduces some gains with limits, the second option is a proposal that accomplishes the five required constraints. On the one hand, it can model tunnels, caves and overhangs, and terrain features can be captured with pixel-perfect accuracy. On the other hand, it is not demanding regarding processing and storage requirements and offers scalability. Finally, rendering, physics and dynamic updates can be performed in real-time.As a result, this work represents a significant contribution, offering an integrated solution capable of addressing the most challenging aspects of dynamic terrains. Our approach introduces a novel terrain model comprising diverse data structures and a suite of algorithms designed to capture a wide range of terrain formations accurately. A scene composed of tens of millions of triangles can be continuously updated to the extent of simulating a completely devastated terrain, rendered, and subjected to real-time physics computations involving tens of thousands of physical entities. The proposed model holds great potential for computer graphic applications, particularly in scenarios such as simulators and games, where dynamic landscapes play a paramount role.
  

• FERRIOL GALMÉS, MIQUEL: Network modeling using graph neural networks
  
  Author: FERRIOL GALMÉS, MIQUEL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: (DAC)
  Mode: Normal
  Deposit date: 10/04/2024
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CABELLOS APARICIO, ALBERTO | BARLET ROS, PERE
  Committee:
       PRESIDENT: PESCAPÈ, ANTONIO
       SECRETARI: ARIAS VICENTE, MARTA
       VOCAL: RÉTVÁRI, GÁBOR
  Thesis abstract: Network modeling is central to the field of computer networks. Models are useful in researching new protocols and mechanisms, allowing administrators to estimate their performance before their actual deployment in production networks. Network models also help to find optimal network configurations, without the need to test them in production networks. Arguably, the most prevalent way to build these network models is through the use of discrete event simulation (DES) methodologies which provide excellent accuracy. State-of-the-art network simulators include a wide range of network, transport, and routing protocols, and are able to simulate realistic scenarios. However, this comes at a very high computational cost that depends linearly on the number of packets being simulated. As a result, they are impractical in scenarios with realistic traffic volumes or large topologies. In addition, and because they are computationally expensive, they do not work well in real-time scenarios.Another network modeling alternative is Queuing Theory (QT) where networks are represented as inter-connected queues that are evaluated analytically. While QT solves the main limitation of DES, it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks.In this context, Machine Learning (ML) has recently emerged as a practical solution to achieve data-driven models that can learn complex traffic models while being extremely accurate and fast. More specifically, Graph Neural Networks (GNNs) have emerged as an excellent tool for modeling graph-structured data showing outstanding accuracy when applied to computer networks. However, some challenges still persist:1. Queues and Scheduling Policies: Modeling queues, scheduling policies, and Quality-of-Service (QoS) mappings within GNN architectures poses another challenge, as these elements are fundamental to network behavior.2. Traffic Models: Accurately modeling realistic traffic patterns, which exhibit strong autocorrelation and heavy tails, remains a challenge for GNN-based solutions.3. Training and Generalization: ML models, including GNNs, require representative training data that covers diverse network operational scenarios. Creating such datasets from real production networks is unfeasible, necessitating controlled testbeds. The challenge lies in designing GNNs capable of accurate estimation in unseen networks, encompassing different topologies, traffic, and configurations.4. Generalization to Larger Networks: Real-world networks are often significantly larger than testbeds. Scaling GNNs to handle networks with hundreds or thousands of nodes is a pressing challenge, one that requires leveraging domain-specific network knowledge and novel architectural approaches.This dissertation represents a step forward in harnessing Graph Neural Networks (GNN models) for network modeling, by proposing a new GNN-based architecture with a focus on addressing these critical challenges while being fast and accurate.