Fourier Transforms

The 21st century ushered in a new era of technology that has been reshaping everyday life, simplifying outdated processes, and even giving rise to entirely new business sectors. Today, contemporary users of products and services expect more and more personalized products and services that can meet their unique needs. In that sense, it is necessary to further develop existing methods, adapt them to new applications, or even discover new methods. This book provides a thorough review of some methods that have an increasing impact on humanity today and that can solve different types of problems even in specific industries. Upgrading with Fourier Transformation gives a different meaning to these methods that support the development of new technologies and have a good projected acceleration in the future

Neural networks in pulsed dipolar spectroscopy : a practical guide

This work was funded by a grant from Leverhulme Trust (RPG-2019-048). Studentship funding and technical support from MathWorks are gratefully acknowledged. This research was supported by grants from NVIDIA and utilised NVIDIA Tesla A100 GPUs through the Academic Grants Programme. We also acknowledge funding from the Royal Society (University Research Fellowship for JEL) and EPSRC (EP/R513337/1 studentship for HR and EP/L015110/1 studentship for MJT).This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs and paramagnetic metal ions. PDS uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their “robust black box” reputation hides the complexity of their design and training – particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against simulated databases, to discuss network architecture choices, to describe options for handling DEER (double electron-electron resonance) and RIDME (relaxation-induced dipolar modulation enhancement) experiments, and to provide a practical data processing flowchart.Publisher PDFPeer reviewe

D˙IFERANS˙IYEL DENKLEMLER˙IN YAPAY S˙IN˙IR AGLARI ˘ ˙ILE NÜMER˙IK ÇÖZÜMLER˙I

D˙IFERANS˙IYEL DENKLEMLER˙IN YAPAY S˙IN˙IR AGLARI ˘ ˙ILE NÜMER˙IK ÇÖZÜMLER˙I ˙Iclal GÖR Doktora Tezi, Matematik Anabilim Dalı Tez Danı¸smanı: Dr. Ögr. Üyesi Korhan GÜNEL ˘ 2020, 91 sayfa Bu çalı¸smada, birinci ve ikinci mertebeden lineer ba¸slangıç deger problemleri, ˘ Dirichlet sınır ko¸sulları içeren ikinci mertebeden lineer ve lineer olmayan diferansiyel denklemler ve birinci mertebeden lineer diferansiyel denklem sistemlerinin nümerik çözümleri ileri beslemeli tek ara katmanlı yapay sinir agları ˘ kullanılarak elde edilmi¸stir. Problemlerin çözümleri için modellenen sinir agları, popülasyon tabanlı global ˘ optimizasyon metotlarından Parçacık Sürü Optimizasyonu, Kütle Çekim Arama Algoritması, Yapay Arı Koloni Algoritması ve Karınca Koloni Optimizasyonu kullanılarak egitilmi¸stir. Ek olarak bahsi geçen optimizasyon algoritmaları ˘ Parçacık Sürü Optimizasyonu algoritması ile hibritlenerek çözümler elde edilmi¸stir. Tez çalı¸sması boyunca incelenen optimizasyon yakla¸sımlarından elde edilen izlenimler dogrultusunda, bilinen en iyi çözümün kom¸sulu ˘ gunda üretilen ˘ hiper-küreleri kullanan yeni bir mutasyon operatörü tanımlanmı¸stır. Deneysel çalı¸smalarda elde edilen bulgular, adi diferansiyel denklemlerin nümerik çözümlerini elde etmede yapay sinir agı kullanımının geleneksel iterasyon tabanlı ˘ yöntemlere göre iyi bir alternatif olabilecegini göstermi¸stir. Yapay sinir a ˘ glarının, ˘ çözüm aranan aralıgın her noktasında tahmini bir de ˘ ger üretebilme yetenekleri bu ˘ yöntemleri klasik yöntemlere göre tercih edilebilir hale getirmektedir. Tezde önerilen yakla¸sım, farklı sabit adım uzunlukları için degi¸sik tipteki ˘ diferansiyel denklemler üzerinde test edilmi¸s ve diger yöntemlerle kıyaslandı ˘ gında ˘ genel olarak benzer veya çogu zaman daha iyi sonuç vermi¸stir. Bununla birlikte, ˘ her tipte diferansiyel denklemi çözebilecek evrensel bir yapay sinir agı modeli ˘ olu¸sturmanın olası olmadıgı kanısına varılmı¸stır.˙IÇ˙INDEK˙ILER KABUL VE ONAY SAYFASI . . . . . . . . . . . . . . . . . . . . . . . . iii B˙IL˙IMSEL ET˙IK B˙ILD˙IR˙IM SAYFASI . . . . . . . . . . . . . . . . . . . v ÖZET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix ÖNSÖZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi S˙IMGELER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv ¸SEK˙ILLER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Ç˙IZELGELER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . xix 1. G˙IR˙I ¸S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. MATERYAL VE METOT . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1. ˙Ileri Beslemeli Yapay Sinir Agları ile Diferansiyel Denklemlerin ˘ Nümerik Çözümleri . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2. Diferansiyel Denklem Sistemlerinin Çözümleri . . . . . . . . . . . . 15 2.3. Popülasyon Tabanlı Global Optimizasyon Yakla¸sımları . . . . . . . . 17 2.3.1. Parçacık Sürü Optimizasyonu . . . . . . . . . . . . . . . . . . . . 18 2.3.2. Kütle Çekim Arama Algoritması . . . . . . . . . . . . . . . . . . . 21 2.3.3. Yapay Arı Koloni Algoritması . . . . . . . . . . . . . . . . . . . . 25 2.3.3.1. Yapay Arı Koloni Algoritması için Yeni Bir Mutasyon Önerisi . . . 28 2.3.4. Karınca Koloni Optimizasyonu . . . . . . . . . . . . . . . . . . . 31 3. DENEYSEL ÇALI ¸SMALAR . . . . . . . . . . . . . . . . . . . . . . . 37 4. TARTI ¸SMA VE SONUÇ . . . . . . . . . . . . . . . . . . . . . . . . . 70 KAYNAKLAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 EKLER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 A. EKLER D˙IZ˙IN˙I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 ÖZGEÇM˙I ¸S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Surrogate Models Coupled with Machine Learning to Approximate Complex Physical Phenomena Involving Aerodynamic and Aerothermal Simulations

Numerical simulations provide a key element in aircraft design process, complementing physical tests and flight tests. They could take advantage of innovative methods, such as artificial intelligence technologies spreading in aviation. Simulating the full flight mission for various disciplines pose important problems due to significant computational cost coupled to varying operating conditions. Moreover, complex physical phenomena can occur. For instance, the aerodynamic field on the wing takes different shapes and can encounter shocks, while aerothermal simulations around nacelle and pylon are sensitive to the interaction between engine flows and external flows. Surrogate models can be used to substitute expensive high-fidelitysimulations by mathematical and statistical approximations in order to reduce overall computation cost and to provide a data-driven approach. In this thesis, we propose two developments: (i) machine learning-based surrogate models capable of approximating aerodynamic experiments and (ii) integrating more classical surrogate models into industrial aerothermal process. The first approach mitigates aerodynamic issues by separating solutions with very different shapes into several subsets using machine learning algorithms. Moreover, a resampling technique takes advantage of the subdomain decomposition by adding extra information in relevant regions. The second development focuses on pylon sizing by building surrogate models substitutingaerothermal simulations. The two approaches are applied to aircraft configurations in order to bridge the gap between academic methods and real-world applications. Significant improvements are highlighted in terms of accuracy and cost gain

Neural Operator: Learning Maps Between Function Spaces

The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks tailored to learn operators mapping between infinite dimensional function spaces. We formulate the approximation of operators by composition of a class of linear integral operators and nonlinear activation functions, so that the composed operator can approximate complex nonlinear operators. Furthermore, we introduce four classes of operator parameterizations: graph-based operators, low-rank operators, multipole graph-based operators, and Fourier operators and describe efficient algorithms for computing with each one. The proposed neural operators are resolution-invariant: they share the same network parameters between different discretizations of the underlying function spaces and can be used for zero-shot super-resolutions. Numerically, the proposed models show superior performance compared to existing machine learning based methodologies on Burgers' equation, Darcy flow, and the Navier-Stokes equation, while being several order of magnitude faster compared to conventional PDE solvers

Dimension Reduction of Neural Models Across Multiple Spatio-temporal Scales

In general, reducing the dimensionality of a complex model is a natural first step to gaining insight into the system. In this dissertation, we reduce the dimensions of models at three different scales: first at the scale of microscopic single-neurons, second at the scale of macroscopic infinite neurons, and third at an in-between spatial scale of finite neural populations. Each model also exhibits a separation of timescales, making them amenable to the method of multiple timescales, which is the primary dimension-reduction tool of this dissertation. In the first case, the method of multiple timescales reduces the dynamics of two coupled n-dimensional neurons into one scalar differential equation representing the slow timescale phase-locking properties of the oscillators as a function of an exogenous slowly varying parameter. This result extends the classic theory of weakly coupled oscillators. In the second case, the method reduces the many spatio-temporal \yp{dynamics of} ``bump'' solutions of a neural field model into its scalar coordinates, which are much easier to analyze analytically. This result generalizes existing studies on neural field spatio-temporal dynamics to the case of a smooth firing rate function and general even kernel. In the third case, we reduce the dimension of the oscillators at the spiking level -- similar to the first case -- but with additional slowly varying synaptic variables. This result generalizes existing studies that use scalar oscillators and the Ott-Antonsen ansatz to reduce the dimensionality and determine the synchronization properties of large neural populations

Kernel - based continous - time systems identification: methods and tools

2012/2013Questa tesi ha lo scopo di formalizzare un nuovo filone teorico, che deriva dall’algebra degli operatori lineari integrali di Fredholm-Volterra agenti su spazi di Hilbert, per la sintesi di stimatori dello stato e parametrici per sistemi dinamici a tempo continuo sfruttando le misure ingressi/uscite, soggetti a perturbazione tempo-varianti. In maniera da ottenere stime non-asintotiche di sistemi dinamici a tempo continuo, i metodi classici tipicamente aumentano la dimensione del vettore delle variabili di decisione con le condizioni iniziali incognite di stati non misurati. Tuttavia, questo porta ad un accrescimento della complessitá dell’algoritmo. Recentemente, diversi metodi di stima algebrici sono stati sviluppati, sfruttando un approccio algebrico piuttosto che da una prospettiva statistica o teorica. Mentre le forti fondamenta teoriche e le proprietá di convergenza non asintotiche rappresentano caratteristiche notevoli per questi metodi, il principale inconveniente é che l’implementazione pratica produce una dinamica internamente instabile. Quindi, la progettazione di metodi di stima per questi tipi di sistemi é un argomento importante ed emergente. L’obiettivo di questo lavoro é quello di presentare alcuni risultati recenti, considerando diversi aspetti e affrontando alcuni dei problemi che emergono quando si progettano algoritmi di identificazione. Lo scopo é sviluppare un’architettura di stima con proprietá di convergenza molto veloci e internamente stabile. Seguendo un ordine logico, prima di tutto verrá progettato l’algoritmo di identificazione proponendo una nuova architettura basata sui kernel, utilizzando l’algebra degli operatori lineari integrali di Fredholm-Volterra. Inoltre, la metodologia proposta sará affrontata in maniera da progettare stimatori per sistemi dinamici a tempo continuo con proprietá di convergenza molto veloci, caratterizzati da gradi relativi limitati e possibilmente affetti da perturbazioni strutturate. Piú nello specifico, il progetto di adeguati kernel di operatori lineari integrali non-anticipativi dará origine a stimatori caratterizzati da proprietá di convergenza idealmente "non- asintotiche".Le analisi delle proprietá dei kernel verrá affrontata e due classi di funzioni kernel ammissibili saranno introdotte: una per il problema di stima parametrica e uno per il problema di stima dello stato. Gli operatori che verranno indotti da tali funzioni kernel proposte, ammettono realizzazione spazio-stato implementabile (cioé a dimensione finita e internamente stabile). Allo scopo di dare maggior completezza, l’analisi del bias dello stimatore proposto verrá esaminata, derivando le proprietá asintotiche dell’algoritmo di identificazione e dimostrando che le funzioni kernel possono essere pro- gettate tenendo in debito conto i risultati ottenuti in questa analisi.This thesis is aimed at the formalization of a new theoretical framework, arising from the algebra of Fredholm-Volterra linear integral operators acting on Hilbert spaces, for the synthesis of non-asymptotic state and parameter estimators for continuous-time dynamical systems from input-output measurements subject to time-varying perturbations. In order to achieve non-asymptotic estimates of continuous-time dynamical systems, classical methods usually augment the vector of decision variables with the unknown initial conditions of the non measured states. However, this comes at the price of an increase of complexity for the algorithm. Recently, several algebraic estimation methods have been developed, arising from an algebraic setting rather than from a statistical or a systems-theoretic perspective. While the strong theoretical foundations and the non-asymptotic convergence property represent oustanding features of these methods, the major drawback is that the practical implementation ends up with an internally unstable dynamic. Therefore, the design of estimation methods for these kind of systems is an important and emergent topic. The goal of this work is to present some recent results, considering different frameworks and facing some of the issues emerging when dealing with the design of identification algorithms. The target is to develop a comprehensive estimation architecture with fast convergence properties and internally stable. Following a logical order, first of all we design the identification algorithm by proposing a novel kernel-based architecture, by means of the algebra of Fredholm-Volterra linear integral operators. Besides, the proposed methodology is addressed in order to design estimators with very fast convergence properties for continuous-time dynamic systems characterized by bounded relative degree and possibly affected by structured perturbations. More specifically, the design of suitable kernels of non-anticipative linear integral operators gives rise to estimators characterized by convergence properties ideally “non-asymptotic". The analysis of the properties of the kernels guaranteeing such a fast convergence is addressed and two classes of admissible kernel functions are introduced: one for the parameter estimation problem and one for the state estimation problem. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state- space realizations. For the sake of completeness, the bias analysis of the proposed estimator is addressed, deriving the asymptotic properties of the identification algorithm and demonstrating that the kernel functions can be designed taking in account the results obtained with this analysis.XXVI Ciclo198