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Dissertations / Theses on the topic 'Physics Informed Neural Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Ding, Simon. "Advancing cosmological field-level inference with physics-informed Bayesian neural networks." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS050.

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La cosmologie repose essentiellement sur des observations passives, obtenues avec des télescopes sophistiqués, pour comprendre l'origine, la dynamique et le destin ultime de l'Univers. Les nouveaux relevés de galaxies propulsent la cosmologie dans une ère dominée par les données, nécessitant des adaptations significatives de nos méthodes d'analyse. L'un des principaux défis de la cosmologie moderne est d'extraire des informations physiques significatives des prochains relevés cosmologiques. Parallèlement à cette révolution, l'apprentissage automatique s'impose comme une technique puissante pou

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Mirzai, Badi. "Physics-Informed Deep Learning for System Identification of Autonomous Underwater Vehicles : A Lagrangian Neural Network Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301626.

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In this thesis, we explore Lagrangian Neural Networks (LNNs) for system identification of Autonomous Underwater Vehicles (AUVs) with 6 degrees of freedom. One of the main challenges of AUVs is that they have limited wireless communication and navigation under water. AUVs operate under strict and uncertain conditions, where they need to be able to navigate and perform tasks in unknown ocean environments with limited and noisy sensor data. A crucial requirement for localization and adaptive control of AUVs is having an accurate and reliable model of the system’s nonlinear dynamics while taking i

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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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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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Jing, Li Ph D. Massachusetts Institute of Technology. "Physical symmetry enhanced neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128294.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, February, 2020<br>Cataloged from student-submitted PDF version of thesis<br>Includes bibliographical references (pages 91-99).<br>Artificial Intelligence (AI), widely considered "the fourth industrial revolution", has shown its potential to fundamentally change our world. Today's AI technique relies on neural networks. In this thesis, we propose several physical symmetry

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Elhawary, Mohamed. "Apprentissage profond informé par la physique pour les écoulements complexes." Electronic Thesis or Diss., Paris, ENSAM, 2024. http://www.theses.fr/2024ENAME068.

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Ce travail de doctorat étudie deux problèmes spécifiques concernant les turbomachines en utilisant des algorithmes d'apprentissage automatique. Le premier se concentre sur un compresseur axial, en abordant les problèmes de décrochage tournant, qui sont des phénomènes instables limitant la plage de fonctionnement des compresseurs. Les avancées récentes comprennent le développement de techniques de contrôle d'écoulement, telles que des jets au niveau du carter et du bord d’attaque du rotor, qui ont montré un potentiel pour étendre les plages de fonctionnement des compresseurs. Cependant, l’optim

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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory f

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9

Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a n

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Gyawali, Gaurav. "Solving Atomic Wave Functions Using Artificial Neural Networks." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/honors_theses/104.

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Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the

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11

DuÌˆring, Alexander. "Temporal aspects of spin-glass neural networks." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325892.

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Wu, Dawen. "Solving Some Nonlinear Optimization Problems with Deep Learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG083.

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Cette thèse considère quatre types de problèmes d'optimisation non linéaire, à savoir les jeux de bimatrice, les équations de projection non linéaire (NPEs), les problèmes d'optimisation convexe non lisse (NCOPs) et les jeux à contraintes stochastiques (CCGs). Ces quatre classes de problèmes d'optimisation non linéaire trouvent de nombreuses applications dans divers domaines tels que l'ingénierie, l'informatique, l'économie et la finance. Notre objectif est d'introduire des algorithmes basés sur l'apprentissage profond pour calculer efficacement les solutions optimales de ces problèmes d'optim

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Tolley, Emma Elizabeth. "Monte Carlo event reconstruction implemented with artificial neural networks." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65535.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 41).<br>I implemented event reconstruction of a Monte Carlo simulation using neural networks. The OLYMPUS Collaboration is using a Monte Carlo simulation of the OLYMPUS particle detector to evaluate systematics and reconstruct events. This simulation registers the passage of particles as 'hits' in the detector elements, which can be used to determine event parameters such as momentum and direction. However, these hits are often obscur

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Doriat, Aurélien. "Caractérisation des couplages aéro-thermo-mécaniques lors d’un vieillissement par thermo-oxydation de composites à matrice polymère soumis à un écoulement rapide et chauffé." Electronic Thesis or Diss., Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2024. http://www.theses.fr/2024ESMA0018.

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Les matériaux composites à matrice organique renforcés de fibres de carbone (CFRP) sont largement utilisés dans les structures aéronautiques froides. Dans les applications de moteurs aéronautiques, comme les aubes de FAN, ces matériaux peuvent être soumis à des conditions environnementales particulièrement sévères, avec des températures pouvant atteindre 120°C et une vitesse d’écoulement proche de Mach 1.Il est bien établi que les polymères époxy sont sujets à des phénomènes de thermo-oxydation lorsqu’ils sont exposés à des températures élevées. Ce phénomène implique la diffusion et la réactio

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Penney, Richard William. "The statistical mechanics of neural networks and spin glasses." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239333.

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Andersson, Mikael. "Gamma-ray racking using graph neural networks." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298610.

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While there are existing methods of gamma ray-track reconstruction in specialized detectors such as AGATA, including backtracking and clustering, it is naturally of interest to diversify the portfolio of available tools to provide us viable alternatives. In this study some possibilities found in the field of machine learning were investigated, more specifically within the field of graph neural networks. In this project there was attempt to reconstruct gamma tracks in a germanium solid using data simulated in Geant4. The data consists of photon energies below the pair production limit and so we

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Andersson, Mikael. "Gamma-ray tracking using graph neural networks." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298610.

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While there are existing methods of gamma ray-track reconstruction in specialized detectors such as AGATA, including backtracking and clustering, it is naturally of interest to diversify the portfolio of available tools to provide us viable alternatives. In this study some possibilities found in the field of machine learning were investigated, more specifically within the field of graph neural networks. In this project there was attempt to reconstruct gamma tracks in a germanium solid using data simulated in Geant4. The data consists of photon energies below the pair production limit and so we

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Cardoso, Mário. "Study of pattern recognition of particle tracks with neural networks." Thesis, Uppsala universitet, Högenergifysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454374.

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In this project we study the use of neural networks as a tool for particle track pattern recognition with the possibility of its implementation in the Trigger system at the ATLAS experiment [1]. By using a method named Hough transform we created a Convolutional Neural Network (CNN) that is able to train on the transformed images of muons merged with minimum bias. We give an overview of how the CNN works and compare the results from the CNN with the old cut based method. We believe to have managed to find an alternative to the previously used algorithm, that is faster and more efficient.

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Skirlo, Scott Alexander. "Photonics for technology : circuits, chip-scale LIDAR, and optical neural networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112519.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 163-175).<br>This thesis focuses on a wide range of contemporary topics in modern electromagnetics and technology including topologically protected one-way modes, integrated photonic LIDAR, and optical neural networks. First, we numerically investigate large Chern numbers in photonic crystals and explore their origin from simultaneously gapping multiple band degeneracies. Following this, we perform microwave transmission meas

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Macpherson, Keith P. "Prediction of solar and geomagnetic activity using artificial neural networks." Thesis, University of Glasgow, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296375.

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Davila, Carlos Antonio. "Image super-resolution performance of multilayer feedforward neural networks." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/284549.

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Super-resolution is the process by which the bandwidth of a diffraction-limited spectrum is extended beyond the optical passband. Many algorithms exist which are capable of super-resolution; however most are iterative methods, which are ill-suited for real-time operation. One approach that has been virtually ignored in super-resolution research is the neural network approach. The Hopfield network has been a popular choice in image restoration applications, however it is also an iterative approach. We consider the feedforward architecture known as a Multilayer Perceptron (MLP), and present resu

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Hodges, Jonathan Lee. "Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86364.

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Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using computational fluid dynamics (CFD) models come at a significant computational cost where simulation times are often measured in hours, days, or even weeks. A new simulation method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make predictions of new inputs. The field of image analysis has seen significant advancements in ANN

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Ajay, Anurag. "Augmenting physics simulators with neural networks for model learning and control." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122747.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-81).<br>Physics simulators play an important role in robot state estimation, planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Therefore, most physics simulators employ approximations that lead to a loss in precision. We propose a hybrid dynamics model, combining a deterministic physical simulator with

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Sánchez, Carlos Andrés. "Measurement of the Top Quark Mass with Neural Networks." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039046089.

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Roy, Chandan. "An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting." Doctoral thesis, Linköpings universitet, Interaktiva och kognitiva system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123198.

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Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning system

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Rosten, David Paul 1967. "Automatic design of a decision tree classifier employing neural networks." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277881.

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Pattern recognition problems involve two main issues: feature formulation and classifier design. This thesis is concerned with the latter. Numerous algorithms for the design of pattern recognition systems have been published, and the algorithm detailed herein is a new approach--specific to the design of decision tree classifiers. It involves a top-down strategy, optimizing the root node decision and then subsequently its children. To assess various pattern space partitions, the Tie statistical distance measure quantified the separability of potential cluster groupings. Additionally, a separate

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Stevenson, King Douglas Beverley. "Robust hardware elements for weightless artificial neural networks." Thesis, University of Central Lancashire, 2000. http://clok.uclan.ac.uk/1884/.

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This thesis investigates novel robust hardware elements for weightless artificial neural systems with a bias towards high integrity avionics applications. The author initially reviews the building blocks of physiological neural systems and then chronologically describes the development of weightless artificial neural systems. Several new design methodologies for the implementation of robust binary sum-and-threshold neurons are presented. The new techniques do not rely on weighted binary counters or registered arithmetic units for their operation making them less susceptible to transient single

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Hedström, Lucas. "Classifying the rotation of bacteria using neural networks." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160518.

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Bacteria can quickly spread throughout the human body, making certain diseases hard or impossible to cure. In order to understand how the bacteria can initiate and develop into an infection, microfluidic chambers in a lab environment are used as a template of how bacteria reacts to different types of flows. However, accurately tracking the movement of bacteria is a difficult task, where small objects has to be captured with a high resolution and digitally analysed with computationally heavy methods. Popular imaging methods utilise digital holographic microscopy, where three-dimensional movemen

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Rigon, Luca. "Development of an intelligent trigger system based on deep neural networks." Thesis, Uppsala universitet, Högenergifysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446864.

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30

Ciobanu, CÄƒtÄƒlin Irinel. "A neural networks search for single top quark production in CDF Run I Data /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486461246815552.

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Mignacco, Francesca. "Statistical physics insights on the dynamics and generalisation of artificial neural networks." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPASP074.

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L'apprentissage machine est une technologie désormais omniprésente dans notre quotidien. Toutefois, ce domaine reste encore largement empirique et ses enjeux scientifiques manquent d'une compréhension théorique profonde. Cette thèse se penche vers la découverte des mécanismes sous-tendant l'apprentissage dans les réseaux de neurones artificiels à travers le prisme de la physique statistique. Dans une première partie, nous nous intéressons aux propriétés statiques des problèmes d'apprentissage, que nous introduisons au chapitre 1.1. Dans le chapitre 1.2, nous considérons la classification d'un

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32

Harris, William H. (William Hunt). "Machine learning transferable physics-based force fields using graph convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128979.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 22-24).<br>Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accur

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Watkin, Timothy L. H. "The theory of quenched disorder : spin glasses, neural networks and statistical inference." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315731.

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Reis, Elohim Fonseca dos 1984. "Criticality in neural networks = Criticalidade em redes neurais." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276917.

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Orientadores: José Antônio Brum, Marcus Aloizio Martinez de Aguiar<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin<br>Made available in DSpace on 2018-08-29T15:40:55Z (GMT). No. of bitstreams: 1 Reis_ElohimFonsecados_M.pdf: 2277988 bytes, checksum: 08f2c3b84a391217d575c0f425159fca (MD5) Previous issue date: 2015<br>Resumo: Este trabalho é dividido em duas partes. Na primeira parte, uma rede de correlação é construída baseada em um modelo de Ising em diferentes temperaturas, crítica, subcrítica e supercrítica, usando um algorítimo de Metropolis

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Varas, Jaime Armando. "Employment of neural networks in the estimation of impact parameters." Thesis, The University of Sydney, 2002. https://hdl.handle.net/2123/27885.

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In this study, we employed a two-stage backpropagation neural network (NNW) to estimate the impact parameter (b) of heavy-ion collisions. Using Monte-Carlo (MC) generated Pb-Pb events at 160 GeV/nucleon we employed three observables from each event to train the NNW. The generated events were target-projectile in nature, from which the charged pion multiplicity (MULT), largest spectator fragment (ZMAX) and charge flow in the forward direction (sz) were used as input signals for the NNW. A statistical approach that employed the weighted mean of the three inputs to estimate b was used as

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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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Battista, Aldo. "Low-dimensional continuous attractors in recurrent neural networks : from statistical physics to computational neuroscience." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLE012.

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La manière dont l'information sensorielle est codée et traitée par les circuits neuronaux est une question centrale en neurosciences computationnelles. Dans de nombreuses régions du cerveau, on constate que l'activité des neurones dépend fortement de certains corrélats sensoriels continus ; on peut citer comme exemples les cellules simples de la zone V1 du cortex visuel codant pour l'orientation d'une barre présentée à la rétine, et les cellules de direction de la tête dans le subiculum ou les cellules de lieu dans l'hippocampe, dont les activités dépendent, respectivement, de l'orientation de

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Ronchi, Emanuele. "Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics." Doctoral thesis, Uppsala universitet, Institutionen för fysik och astronomi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-108583.

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This thesis describes the development of electronic modules for fusion neutron spectroscopy as well as several implementations of artificial neural networks (NN) for neutron diagnostics for the Joint European Torus (JET) experimental reactor in England. The electronics projects include the development of two fast light pulser modules based on Light Emitting Diodes (LEDs) for the calibration and stability monitoring of two neutron spectrometers (MPRu and TOFOR) at JET. The particular electronic implementation of the pulsers allowed for operation of the LEDs in the nanosecond time scale, which i

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Sung, Woong Je. "A neural network construction method for surrogate modeling of physics-based analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43721.

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A connectivity adjusting learning algorithm, Optimal Brain Growth (OBG) was proposed. Contrast to the conventional training methods for the Artificial Neural Network (ANN) which focus on the weight-only optimization, the OBG method trains both weights and connectivity of a network in a single training process. The standard Back-Propagation (BP) algorithm was extended to exploit the error gradient information of the latent connection whose current weight has zero value. Based on this, the OBG algorithm makes a rational decision between a further adjustment of an existing connection weight and a

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Pusuluri, Sai Teja. "Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1490116134938074.

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Ericsson, Oscar. "Investigations into neutrino flavor reconstruction from radio detector data using convolutional neural networks." Thesis, Uppsala universitet, Högenergifysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-449503.

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As the IceCube Neutrino Observatory seeks to expand its sensitivity to high PeV-EeV energies by means of the radio technique, the need for fast, efficient and reliable reconstruction methods to recover neutrino properties from radio detector data has emerged. The first recorded investigation into the possibilities of using a neural network based approach to flavor reconstruction is presented. More specifically, a deep convolutional neural network was built and optimized for the purpose of differentiating νe charged current (CC) interaction events from events of all other flavors and interactio

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Posani, Lorenzo. "Inference and modeling of biological networks : a statistical-physics approach to neural attractors and protein fitness landscapes." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE043/document.

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L'avènement récent des procédures expérimentales à haut débit a ouvert une nouvelle ère pour l'étude quantitative des systèmes biologiques. De nos jours, les enregistrements d'électrophysiologie et l'imagerie du calcium permettent l'enregistrement simultané in vivo de centaines à des milliers de neurones. Parallèlement, grâce à des procédures de séquençage automatisées, les bibliothèques de protéines fonctionnelles connues ont été étendues de milliers à des millions en quelques années seulement. L'abondance actuelle de données biologiques ouvre une nouvelle série de défis aux théoriciens. Des

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Grose, Mitchell. "Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788.

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Canaday, Daniel M. "Modeling and Control of Dynamical Systems with Reservoir Computing." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157469471458874.

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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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Meseguer, Brocal Gabriel. "Multimodal analysis : informed content estimation and audio source separation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS111.

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Cette thèse propose l'étude de l'apprentissage multimodal dans le contexte de signaux musicaux. Tout au long de ce manuscrit, nous nous concentrerons sur l'interaction entre les signaux audio et les informations textuelles. Parmi les nombreuses sources de texte liées à la musique qui peuvent être utilisées (par exemple les critiques, les métadonnées ou les commentaires des réseaux sociaux), nous nous concentrerons sur les paroles. La voix chantée relie directement le signal audio et les informations textuelles d'une manière unique, combinant mélodie et paroles où une dimension linguistique com

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SAGLIETTI, LUCA. "Out of equilibrium Statistical Physics of learning." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710532.

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In the study of hard optimization problems, it is often unfeasible to achieve a full analytic control on the dynamics of the algorithmic processes that find solutions efficiently. In many cases, a static approach is able to provide considerable insight into the dynamical properties of these algorithms: in fact, the geometrical structures found in the energetic landscape can strongly affect the stationary states and the optimal configurations reached by the solvers. In this context, a classical Statistical Mechanics approach, relying on the assumption of the asymptotic realization of a B

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Marginean, Radu. "Measurement of the top pair production cross section at CDF using neural networks." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1101831484.

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Thesis (Ph. D.)--Ohio State University, 2004.<br>Title from first page of PDF file. Document formatted into pages; contains xiii, 110 p.; also includes graphics (some col.). Includes bibliographical references (p. 106-110).

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Zhao, Ruiguang. "Development of a CMOS pixel sensor with on-chip artificial neural networks." Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAE050.

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Dans le détecteur de vertex de l'ILC (International Linear Collider), un nombre élevé d'impacts supplémentaires seront générés par des électrons résultant de processus liés au bruit de fond des faisceaux. Leur impulsion se trouve typiquement est inférieure à celle des particules issues d'événements associés à des processus physiques. Notre groupe à l'IPHC a proposé d'explorer le concept d'un capteur à pixels CMOS avec des ANNs intégrés pour marquer et supprimer les pixels touchés (hits) générés par ces électrons.Au cours de ma thèse de doctorat, je me suis concentré sur l'étude d'un capteur à

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Grassia, Filippo. "Silicon neural networks : implementation of cortical cells to improve the artificial-biological hybrid technique." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00789406.

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This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simul

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