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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
61

Accelerating bulk material property prediction using machine learning potentials for molecular dynamics : predicting physical properties of bulk Aluminium and Silicon / Acceleration av materialegenskapers prediktion med hjälp av maskininlärda potentialer för molekylärdynamik

Sepp Löfgren, Nicholas January 2021 (has links)
In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). The simulations using these ML potentials are then compared with results obtained from ab-initio simulations using the gold standard method of density functional theory (DFT), as implemented in the Vienna ab-intio simulation package (VASP). The results show that the MTP simulations reach comparable accuracy compared to the DFT simulations for the properties total energy and MSD for Aluminium, with errors in the orders of magnitudes of meV and 10-5 Å2. Specific heat capacity is not reasonably replicated for Aluminium. The MTP simulations do not reasonably replicate the studied properties for the system of Silicon. The KRR models are implemented in the most direct way, and do not yield reasonably low errors even when trained on all available 10000 time steps of DFT training data. On the other hand, the MTPs require only to be trained on approximately 100 time steps to replicate the physical properties of Aluminium with accuracy comparable to DFT. After being trained on 100 time steps, the trained MTPs achieve mean absolute errors in the orders of magnitudes for the energy per atom and force magnitude predictions of 10-3 and 10-1 respectively for Aluminium, and 10-3 and 10-2 respectively for Silicon. At the same time, the MTP simulations require less core hours to simulate the same amount of time steps as the DFT simulations. In conclusion, MTPs could very likely play a role in accelerating both materials simulations themselves and subsequently the emergence of the data-driven materials design and informatics paradigm.
62

Pénalisation et réduction de la dimension des variables auxiliaires en théorie des sondages / Penalization and data reduction of auxiliary variables in survey sampling

Shehzad, Muhammad Ahmed 12 October 2012 (has links)
Les enquêtes par sondage sont utiles pour estimer des caractéristiques d'une populationtelles que le total ou la moyenne. Cette thèse s'intéresse à l'étude detechniques permettant de prendre en compte un grand nombre de variables auxiliairespour l'estimation d'un total.Le premier chapitre rappelle quelques définitions et propriétés utiles pour lasuite du manuscrit : l'estimateur de Horvitz-Thompson, qui est présenté commeun estimateur n'utilisant pas l'information auxiliaire ainsi que les techniques decalage qui permettent de modifier les poids de sondage de facon à prendre encompte l'information auxiliaire en restituant exactement dans l'échantillon leurstotaux sur la population.Le deuxième chapitre, qui est une partie d'un article de synthèse accepté pourpublication, présente les méthodes de régression ridge comme un remède possibleau problème de colinéarité des variables auxiliaires, et donc de mauvais conditionnement.Nous étudions les points de vue "model-based" et "model-assisted" dela ridge regression. Cette technique qui fournit de meilleurs résultats en termed'erreur quadratique en comparaison avec les moindres carrés ordinaires peutégalement s'interpréter comme un calage pénalisé. Des simulations permettentd'illustrer l'intérêt de cette technique par compar[a]ison avec l'estimateur de Horvitz-Thompson.Le chapitre trois présente une autre manière de traiter les problèmes de colinéaritévia une réduction de la dimension basée sur les composantes principales. Nousétudions la régression sur composantes principales dans le contexte des sondages.Nous explorons également le calage sur les moments d'ordre deux des composantesprincipales ainsi que le calage partiel et le calage sur les composantes principalesestimées. Une illustration sur des données de l'entreprise Médiamétrie permet deconfirmer l'intérêt des ces techniques basées sur la réduction de la dimension pourl'estimation d'un total en présence d'un grand nombre de variables auxiliaires / Survey sampling techniques are quite useful in a way to estimate population parameterssuch as the population total when the large dimensional auxiliary data setis available. This thesis deals with the estimation of population total in presenceof ill-conditioned large data set.In the first chapter, we give some basic definitions that will be used in thelater chapters. The Horvitz-Thompson estimator is defined as an estimator whichdoes not use auxiliary variables. Along with, calibration technique is defined toincorporate the auxiliary variables for sake of improvement in the estimation ofpopulation totals for a fixed sample size.The second chapter is a part of a review article about ridge regression estimationas a remedy for the multicollinearity. We give a detailed review ofthe model-based, design-based and model-assisted scenarios for ridge estimation.These estimates give improved results in terms of MSE compared to the leastsquared estimates. Penalized calibration is also defined under survey sampling asan equivalent estimation technique to the ridge regression in the classical statisticscase. Simulation results confirm the improved estimation compared to theHorvitz-Thompson estimator.Another solution to the ill-conditioned large auxiliary data is given in terms ofprincipal components analysis in chapter three. Principal component regression isdefined and its use in survey sampling is explored. Some new types of principalcomponent calibration techniques are proposed such as calibration on the secondmoment of principal component variables, partial principal component calibrationand estimated principal component calibration to estimate a population total. Applicationof these techniques on real data advocates the use of these data reductiontechniques for the improved estimation of population totals

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