Numerical study on viscoelastic behavior of polypropylene fiber reinforced concrete subjected to temperature variations using LDPM theory

This thesis is focused on the viscoelastic behavior of macro-synthetic fiber-reinforced concrete (MSFRC) with polypropylene studied numerically when subjected to temperature variations (-30 oC to +60 oC). LDPM (lattice discrete particle model), a meso-scale model for heterogeneous composites, is used. To reproduce the MSFRC structural behavior, an extended version of LDPM that includes fiber effects through fiber-concrete interface micromechanics, called LDPM-F, is applied. Model calibration is performed based on three-point bending, cube, and cylinder test for plain concrete and MSFRC. This is followed by a comprehensive literature study on the variation of mechanical properties with temperature for individual fibers and plain concrete. This literature study and past experimental test results constitute inputs for final numerical simulations. The numerical response of MSFRC three-point bending test is replicated and compared with the previously conducted experimental test results; finally, the conclusions were drawn. LDPM numerical model is successfully calibrated using experimental responses on plain concrete. Fiber-concrete interface micro-mechanical parameters are subsequently fixed and LDPM-F models are calibrated based on MSFRC three-point bending test at room temperature. Number of fibers contributing crack bridging mechanism is computed and found to be in good agreement with experimental counts. Temperature variations model for individual constituents of MSFRC, fibers and plain concrete, are implemented in LDPM-F. The model is validated for MSFRC three-point bending stress-CMOD (crack mouth opening) response reproduced at -30 oC, -15 oC, 0 oC, +20 oC, +40 oC and +60 oC. It is found that the model can well describe the temperature variation behavior of MSFRC. At positive temperatures, simulated responses are in good agreement. Slight disagreement in negative regimes suggests an in-depth study on fiber-matrix interface bond behavior with varying temperatures.

Stochastic local volatility model for fx markets

Questa tesi verte sullo studio di un modello a volatilità stocastica e locale, utilizzato per valutare opzioni esotiche nei mercati dei cambio. La difficoltà nell'implementare un modello di tal tipo risiede nella calibrazione della leverage surface e uno degli scopi principali di questo lavoro è quello di mostrarne la procedura.

Jarrow-Yildirim model for inflation: theory and applications

This thesis deals with inflation theory, focussing on the model of Jarrow & Yildirim, which is nowadays used when pricing inflation derivatives. After recalling main results about short and forward interest rate models, the dynamics of the main components of the market are derived. Then the most important inflation-indexed derivatives are explained (zero coupon swap, year-on-year, cap and floor), and their pricing proceeding is shown step by step. Calibration is explained and performed with a common method and an heuristic and non standard one. The model is enriched with credit risk, too, which allows to take into account the possibility of bankrupt of the counterparty of a contract. In this context, the general method of pricing is derived, with the introduction of defaultable zero-coupon bonds, and the Monte Carlo method is treated in detailed and used to price a concrete example of contract. Appendixes: A: martingale measures, Girsanov's theorem and the change of numeraire. B: some aspects of the theory of Stochastic Differential Equations; in particular, the solution for linear EDSs, and the Feynman-Kac Theorem, which shows the connection between EDSs and Partial Differential Equations. C: some useful results about normal distribution.

Mimicking the one-dimensional marginal distributions of stochastic diffusions

In the large maturity limit, we compute explicitly the Local Volatility surface for Heston, through Dupire’s formula, with Fourier pricing of the respective derivatives of the call price. Than we verify that the prices of European call options produced by the Heston model, concide with those given by the local volatility model where the Local Volatility is computed as said above.

Pricing in stochastic-local volatility models with default

In recent years is becoming increasingly important to handle credit risk. Credit risk is the risk associated with the possibility of bankruptcy. More precisely, if a derivative provides for a payment at cert time T but before that time the counterparty defaults, at maturity the payment cannot be effectively performed, so the owner of the contract loses it entirely or a part of it. It means that the payoff of the derivative, and consequently its price, depends on the underlying of the basic derivative and on the risk of bankruptcy of the counterparty. To value and to hedge credit risk in a consistent way, one needs to develop a quantitative model. We have studied analytical approximation formulas and numerical methods such as Monte Carlo method in order to calculate the price of a bond. We have illustrated how to obtain fast and accurate pricing approximations by expanding the drift and diffusion as a Taylor series and we have compared the second and third order approximation of the Bond and Call price with an accurate Monte Carlo simulation. We have analysed JDCEV model with constant or stochastic interest rate. We have provided numerical examples that illustrate the effectiveness and versatility of our methods. We have used Wolfram Mathematica and Matlab.

Stochastic modeling of bacterial protein domains distribution to predict horizontal gene transfer

In questo elaborato, abbiamo tentato di modellizzare i processi che regolano la presenza dei domini proteici. I domini proteici studiati in questa tesi sono stati ottenuti dai genomi batterici disponibili nei data base pubblici (principalmente dal National Centre for Biotechnology Information: NCBI) tramite una procedura di simulazione computazionale. Ci siamo concentrati su organismi batterici in quanto in essi la presenza di geni trasmessi orizzontalmente, ossia che parte del materiale genetico non provenga dai genitori, e assodato che sia presente in una maggiore percentuale rispetto agli organismi più evoluti. Il modello usato si basa sui processi stocastici di nascita e morte, con l'aggiunta di un parametro di migrazione, usato anche nella descrizione dell'abbondanza relativa delle specie in ambito delle biodiversità ecologiche. Le relazioni tra i parametri, calcolati come migliori stime di una distribuzione binomiale negativa rinormalizzata e adattata agli istogrammi sperimentali, ci induce ad ipotizzare che le famiglie batteriche caratterizzate da un basso valore numerico del parametro di immigrazione abbiano contrastato questo deficit con un elevato valore del tasso di nascita. Al contrario, ipotizziamo che le famiglie con un tasso di nascita relativamente basso si siano adattate, e in conseguenza, mostrano un elevato valore del parametro di migrazione. Inoltre riteniamo che il parametro di migrazione sia direttamente proporzionale alla quantità di trasferimento genico orizzontale effettuato dalla famiglia batterica.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Stochastic physics and representation of El Niño Southern Oscillation in climate models

El Niño-Southern Oscillation (ENSO) è il maggiore fenomeno climatico che avviene a livello dell’Oceano Pacifico tropicale e che ha influenze ambientali, climatiche e socioeconomiche a larga scala. In questa tesi si ripercorrono i passi principali che sono stati fatti per tentare di comprendere un fenomeno così complesso. Per prima cosa, si sono studiati i meccanismi che ne governano la dinamica, fino alla formulazione del modello matematico chiamato Delayed Oscillator (DO) model, proposto da Suarez e Schopf nel 1988. In seguito, per tenere conto della natura caotica del sistema studiato, si è introdotto nel modello lo schema chiamato Stochastically Perturbed Parameterisation Tendencies (SPPT). Infine, si sono portati due esempi di soluzione numerica del DO, sia con che senza l’introduzione della correzione apportata dallo schema SPPT, e si è visto in che misura SPPT porta reali miglioramenti al modello studiato.