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Rozprawy doktorskie na temat „Machine Learning Informé”

Autor: Grafiati

Data publikacji: 21 grudnia 2024

Data aktualizacji: 31 lipca 2025

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1

Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.

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Dès la conception, des facteurs environnementaux tels que la qualité de l'air ou les habitudes alimentaires peuvent significativement influencer le risque de développer diverses maladies chroniques. Dans la littérature épidémiologique, des indicateurs connus sous le nom de Scores de Risque Environnemental (Environmental Risk Score, ERS) sont utilisés non seulement pour identifier les individus à risque, mais aussi pour étudier les relations entre les facteurs environnementaux et la santé. Une limite de la plupart des ERSs est qu'ils sont exprimés sous forme de combinaisons linéaires d'un nombr

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Doumèche, Nathan. "Physics-informed machine learning : a mathematical framework with applications to time series forecasting." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS105.

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L'apprentissage automatique informé par la physique est un domaine récent qui consiste à intégrer des connaissances physiques dans des modèles d'apprentissage automatique. L'information physique prend souvent la forme d'un système d'équations aux dérivées partielles (EDPs) que la fonction de régression doit satisfaire. Dans la première partie de cette thèse, nous analysons les propriétés statistiques des méthodes d'apprentissage automatique informé par la physique. En particulier, nous étudions les propriétés des réseaux de neurones informés par la physique, en termes d'approximation, de consi

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Quattromini, Michele. "Graph Neural Networks for fluid mechanics : data-assimilation and optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST161.

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Cette thèse de doctorat explore l'application des réseaux de neurones en graphes (GNN) dans le domaine de la dynamique des fluides numérique (CFD), avec un accent particulier sur l'assimilation de données et l'optimisation. Le travail est structuré en trois parties principales: assimilation de données pour les équations de Navier-Stokes moyennées à la Reynolds (RANS) basée sur des modèles GNN; assimilation de données augmentée par les GNN avec des contraintes physiques imposées par la méthode adjointe; optimisation des systèmes fluides par des techniques d'apprentissage automatique (ML).Dans l

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Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.

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Leung, Jason W. "Application of machine learning : automated trading informed by event driven data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105982.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-65).<br>Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical

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Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.

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Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, a

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Elabid, Zakaria. "Informed deep learning for modeling physical dynamics." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS006.

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La modélisation des systèmes statiques et dynamiques a été révolutionnée par l'apprentissage profond, qui a offert des outils puissants pour la prévision et l'adaptation à des phénomènes physiques complexes. Cependant, les réseaux de neurones traditionnels peinent souvent à se généraliser, en particulier dans des scénarios hors distribution et avec des ensembles de données réduits, ce qui limite leur application dans des contextes réels. Cette thèse aborde ces défis en proposant des méthodes novatrices qui intègrent directement les lois physiques dans les cadres d'apprentissage profond. La pre

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Reichert, Nils. "CORRELATION BETWEEN COMPUTER RECOGNIZED FACIAL EMOTIONS AND INFORMED EMOTIONS DURING A CASINO COMPUTER GAME." Thesis, Fredericton: University of New Brunswick, 2012. http://hdl.handle.net/1882/44596.

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Emotions play an important role for everyday communication. Different methods allow computers to recognize emotions. Most are trained with acted emotions and it is unknown if such a model would work for recognizing naturally appearing emotions. An experiment was setup to estimate the recognition accuracy of the emotion recognition software SHORE, which could detect the emotions angry, happy, sad, and surprised. Subjects played a casino game while being recorded. The software recognition was correlated with the recognition of ten human observers. The results showed a strong recognition for happ

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Wang, Jianxun. "Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77035.

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Computational fluid dynamics (CFD) has been widely used to simulate turbulent flows. Although an increased availability of computational resources has enabled high-fidelity simulations (e.g. large eddy simulation and direct numerical simulation) of turbulent flows, the Reynolds-Averaged Navier-Stokes (RANS) equations based models are still the dominant tools for industrial applications. However, the predictive capability of RANS models is limited by potential inaccuracies driven by hypotheses in the Reynolds stress closure. With the ever-increasing use of RANS simulations in mission-critical a

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Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.

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Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. The first type of PINN was trained to predict an unknown reaction rate law, based only on the differential equation and a time series of the reactor state. The resulting model was used inside a multi-step solver to simulate the system state over time. The results showed that t

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11

Emerson, Guy Edward Toh. "Functional distributional semantics : learning linguistically informed representations from a precisely annotated corpus." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/284882.

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The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors - but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about 'truth', a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word

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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m

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13

Augustin, Lefèvre. "Méthodes d'apprentissage appliquées à la séparation de sources mono-canal." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00764546.

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Étant donne un mélange de plusieurs signaux sources, par exemple un morceau et plusieurs instruments, ou un entretien radiophonique et plusieurs interlocuteurs, la séparation de source mono-canal consiste a' estimer chacun des signaux sources a' partir d'un enregistrement avec un seul microphone. Puisqu'il y a moins de capteurs que de sources, il y a a priori une infinité de solutions sans rapport avec les sources originales. Il faut alors trouver quelle information supplémentaire permet de rendre le problème bien pose. Au cours des dix dernières années, la factorisation en matrices positives

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14

Toure, Carine. "Capitalisation pérenne de connaissances industrielles : Vers des méthodes de conception incrémentales et itératives centrées sur l’activité." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI095/document.

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Dans ce travail de recherche, nous nous intéressons à la question de la pérennité de l’usage des systèmes de gestion des connaissances (SGC) dans les entreprises. Les SGC sont ces environnements informatiques qui sont mis en place dans les entreprises pour mutualiser et construire l’expertise commune grâce aux collaborateurs. Le constat montre que, malgré la rigueur employée par les entreprises pour la mise en œuvre de ces SGC, le risque d’échec des initiatives de gestion des connaissances, notamment lié à l’acceptation de ces environnements par les utilisateurs professionnels ainsi qu’à leur

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15

SIMONETTA, FEDERICO. "MUSIC INTERPRETATION ANALYSIS. A MULTIMODAL APPROACH TO SCORE-INFORMED RESYNTHESIS OF PIANO RECORDINGS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/918909.

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This Thesis discusses the development of technologies for the automatic resynthesis of music recordings using digital synthesizers. First, the main issue is identified in the understanding of how Music Information Processing (MIP) methods can take into consideration the influence of the acoustic context on the music performance. For this, a novel conceptual and mathematical framework named “Music Interpretation Analysis” (MIA) is presented. In the proposed framework, a distinction is made between the “performance” – the physical action of playing – and the “interpretation” – the action that

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Bonantini, Andrea. "Analisi di dati e sviluppo di modelli predittivi per sistemi di saldatura." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24664/.

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Il presente lavoro di tesi ha come obiettivo quello di predire la lunghezza dell'arco elettrico che si forma nel processo di saldatura MIG/MAG, quando un elettrodo fusibile (filo) si porta a una distanza opportuna dal componente che deve essere saldato. In questo caso specifico, la lega di materiale che forma il filo è fatta di Alluminio-Magnesio. In particolare, in questo elaborato sarà presentato l'impatto che avranno alcune grandezze fisiche, come tensione, corrente e velocità di trascinamento del filo che viene fuso, durante il procedimento della saldatura, e come queste influenzeranno la

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17

Santos, Jadson da Silva. "Estudo comparativo de diferentes classificadores baseados em aprendizagem de m?quina para o processo de Reconhecimento de Entidades Nomeadas." Universidade Estadual de Feira de Santana, 2016. http://localhost:8080/tede/handle/tede/554.

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Submitted by Jadson Francisco de Jesus SILVA (jadson@uefs.br) on 2018-01-24T22:42:26Z No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5)<br>Made available in DSpace on 2018-01-24T22:42:26Z (GMT). No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5) Previous issue date: 2016-09-09<br>The Named Entity Recognition (NER) process is the task of identifying relevant termsintextsandassigningthemalabel.Suchwordscanreferencenamesofpeople, organizations, and places. The variety of techniques that can be

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18

Reverdi, Justin. "Vers un capteur virtuel certifiable : évaluation ingenieure, robustesse globale et locale." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2025. http://www.theses.fr/2025TLSEP048.

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Dans les systèmes à cycle de vapeur, le capteur de débit massique joue un rôle clé dans diverses tâches de surveillance et de contrôle. Cependant, les capteurs physiques peuvent être imprécis ou encombrants, et présenter une sensibilité élevée aux vibrations, ce qui est particulièrement problématique lorsque le système est embarqué dans un avion. De plus, ces capteurs peuvent être coûteux et difficiles à entretenir. Pour pallier ces limitations, le développement d’un capteur virtuel basé sur des données issues de capteurs standards apparaît comme une alternative intéressante.Ce manuscrit abord

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Muriithi, Paul Mutuanyingi. "A case for memory enhancement : ethical, social, legal, and policy implications for enhancing the memory." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/a-case-for-memory-enhancement-ethical-social-legal-and-policy-implications-for-enhancing-the-memory(bf11d09d-6326-49d2-8ef3-a40340471acf).html.

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The desire to enhance and make ourselves better is not a new one and it has continued to intrigue throughout the ages. Individuals have continued to seek ways to improve and enhance their well-being for example through nutrition, physical exercise, education and so on. Crucial to this improvement of their well-being is improving their ability to remember. Hence, people interested in improving their well-being, are often interested in memory as well. The rationale being that memory is crucial to our well-being. The desire to improve one’s memory then is almost certainly as old as the desire to

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Ayo, Brenda. "Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements." Master's thesis, 2020. http://hdl.handle.net/10362/93641.

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Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies<br>The identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Sala

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21

Schumacher, Johannes. "Time Series Analysis informed by Dynamical Systems Theory." Doctoral thesis, 2015. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2015061113245.

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This thesis investigates time series analysis tools for prediction, as well as detection and characterization of dependencies, informed by dynamical systems theory. Emphasis is placed on the role of delays with respect to information processing in dynamical systems, as well as with respect to their effect in causal interactions between systems. The three main features that characterize this work are, first, the assumption that time series are measurements of complex deterministic systems. As a result, functional mappings for statistical models in all methods are justified by concepts from dyna

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Rautela, Mahindra Singh. "Hybrid Physics-Data Driven Models for the Solution of Mechanics Based Inverse Problems." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6123.

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Inverse problems pose a significant challenge as they aim to estimate the causal factors that result in a measured response. However, the responses are often truncated, partially available, and corrupted by measurement noise, rendering the problems ill-posed, and may have multiple or no solutions. Solving such problems using regularization transforms them into a family of well-posed functions. While physics-based models are interpretable, they operate under approximations and assumptions. Data-driven models such as machine learning and deep learning have shown promise in solving mechanics-base

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Coimbra, Ana Cecília Sousa Rocha. "Improving clinical problem list with evidence based medicine, patient oriented medical records and intelligence." Doctoral thesis, 2021. http://hdl.handle.net/1822/75189.

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Programa Doutoral em Engenharia Biomédica<br>As listas de problemas clínicos são muito importantes na prestação de cuidados de saúde, principalmente em termos de precisão. A presente tese tem como base um conjunto de estudos realizados no Centro Hospitalar Universitário do Porto, onde o principal objetivo é o de melhorar as listas de problemas clínicos. O primeiro estudo foca-se nos passos iniciais do desenvolvimento de um sistema de registo clínico inovador que utiliza openEHR e terminologia SNOMED CT. Este sistema irá permitir a criação de registos estruturados através da utilizaç

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Yadav, Sangeeta. "Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6095.

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This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with conventional numerical techniques such as Finite Element Methods (FEM) due to the presence of boundary and interior layers. Often the standard numerical solution shows spurious oscillations in the vicinity of these layers. Stabilization techniques are often employed to eliminate these spurious oscillations in the numerical solution. The accuracy of the stabilization techniq

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