Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate's architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints. The hierarchical attribute has two advantages: i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; ii) It accelerates the prediction step by enabling adaptivity as the surrogate's evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-D scenarios, the proposed ML framework can adaptively predict the dynamics of almost thirty species with a maximum relative error of 4.5% for a wide range of initial conditions. Furthermore, when employed in 1-D shock simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to conventional implicit schemes employed in an operator-splitting integration framework. Given the results presented in the paper, this work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena in multi-dimensional computational fluid dynamics simulations

Machine learning and uncertainty quantification framework for predictive ab initio Hypersonics

Hypersonics represents one of the most challenging applications for predictive science. Due to the multi-scale and multi-physics characteristics, high-Mach phenomena are generally complex from both the computational and the experimental perspectives. Nevertheless, the related simulations typically require high accuracy, as their outcomes inform design and decision-making processes in safety-critical applications. Ab initio approaches aim to improve the predictive accuracy by making the calculations free from empiricism. In order to achieve this goal, these methodologies move the computational resolution down to the interatomic level by relying on first-principles quantum physics. As side effects, the increase in model complexity also results in: i) more physics that could be potentially misrepresented and ii) dramatic inflation of the computational cost. This thesis leverages machine learning (ML), uncertainty quantification (UQ), data science, and reduced order models (ROMs) for tackling these downsides and improving the predictive capabilities of ab initio Hypersonics. The first part of the manuscript focuses on formulating and testing a systematic approach to the reliability assessment of ML-based models based on their non-deterministic extensions. In particular, it introduces a novel methodology for the quantification of uncertainties associated with potential energy surfaces (PESs) computed from first-principles quantum mechanical calculations. The methodology relies on Bayesian inference and ML techniques to construct a stochastic PES and to express the inadequacies associated with the ab initio data points and their fit. The resulting stochastic surface is efficiently forward propagated via quasi-classical trajectory (QCT) and master equation calculations by combining high fidelity calculations and reduced order modeling. In this way, the PES contribution to the uncertainty on predefined quantities of interest (QoIs) is explicitly determined. This study is done at both microscopic (e.g., rovibrational-specific rate coefficients) and macroscopic (e.g., thermal and chemical relaxation properties) levels. A correlation analysis is finally applied to identify the PES regions that require further refinement, based on their effects on the QoI reliability. The methodology is applied to the study of singlet (11A') and quintet (25A') PESs describing the interaction between O2 molecules and O atoms in their ground electronic state. The investigation of the singlet surface reveals a negligible uncertainty on the kinetic properties and relaxation times, which are found to be in excellent agreement with the ones previously published in the literature. On the other hand, the methodology demonstrated significant uncertainty on the quintet surface due to inaccuracies in the description of the exchange barrier and the repulsive wall. When forward propagated, this uncertainty is responsible for the variability of one order of magnitude in the vibrational relaxation time and of factor four in the exchange reaction rate coefficient, both at 2,500 K. The second part of this thesis presents a data-informed and physics-driven coarse-graining strategy aimed to reduce the computational cost of ab initio simulations. At first, an in-depth discussion of the physics governing the non-equilibrium dissociation of O2 molecules colliding with O atoms is proposed. A rovibrationally-resolved database for all of the elementary collisional processes is constructed by including all nine adiabatic electronic states of O3 in the QCT calculations. A detailed analysis of the ab initio data set reveals that, for a rovibrational level, the probability of dissociating is mostly dictated by its deficit in internal energy compared to the centrifugal barrier. Due to the assumption of rotational equilibrium, the conventional vibrational-specific calculations fail to characterize such a dependence, and the new ROM strategy is proposed based on this observation. By relying on a hybrid technique made of rovibrationally-resolved excitation coupled to coarse-grained dissociation, the novel approach is compared to the vibrational-specific model and the direct solution of the rovibrational state-to-state master equation. Simulations are performed in a zero-dimensional isothermal and isochoric chemical reactor for a wide range of temperatures (1,500 - 20,000 K). The study shows that the main contribution to the model inadequacy of vibrational-specific approaches originates from the incapability of characterizing dissociation, rather than the energy transfers. Even when constructed with only twenty groups and only 20% of the original computational cost, the new reduced order model outperforms the vibrational-specific one in predicting all of the QoIs related to dissociation kinetics. At the highest temperature, the accuracy in the mole fraction is improved by 2,000%

Progettazione e sviluppo di un sistema di rilevamento delle persone tramite l’uso di una videocamera termica

L'obiettivo di questa tesi è quello di progettare ed implementare un sistema in grado di monitorare un'area e rilevare la presenza di esseri umani. Attraverso la costruzione di una postazione, composta da una videocamera termica collegata ad un single board computer, sarà possibile comunicare al server le immagini acquisite, queste saranno collezionate, analizzate e saranno generati degli eventi che saranno inseriti nella base di dati. Nello svolgimento della tesi saranno esposti vari argomenti, tra cui: il metodo adoperato per rintracciare le persone, le tecnologie usate, la documentazione prodotta durante la fase di progettazione, l'implementazione, l'addestramento di un classificatore a cascata creato appositamente per rilevare individui a mezzobusto ed il confronto con il classificatore usato durante la prima fase di test. Infine, ci sarà una discussione riguardo i risultati ottenuti, in particolare circa l'affidabilità del sistema

NLP-based Metadata Extraction for Legal Text Consolidation

The paper describes a system for the automatic consolidation of Italian legislative texts to be used as a support of an editorial consolidating activity and dealing with the following typology of textual amendments: repeal, substitution and integration. The focus of the paper is on the semantic analysis of the textual amendment provisions and the formalized representation of the amendments in terms of metadata. The proposed approach to consolidation is metadata- oriented and based on Natural Language Processing (NLP) techniques: we use XML-based standards for metadata annotation of legislative acts and a flexible NLP architecture for extracting metadata from parsed texts. An evaluation of achieved results is also provided

Dal testo alla conoscenza e ritorno: estrazione terminologica e annotazione semantica di basi documentali di dominio.

The paper focuses on the automatic extraction of domain knowledge from Italian legal texts and presents a fully-implemented ontology learning system (T2K, Text-2-Knowledge) that includes a battery of tools for Natural Language Processing, statistical text analysis and machine learning. Evaluated results show the considerable potential of systems like T2K, exploiting an incremental interleaving of NLP and machine learning techniques for accurate large-scale semi-automatic extraction and structuring of domain-specific knowledge

Bootstrapping a Verb Lexicon for Biomedical Information Extraction

The accurate extraction of information from texts requires both syntactic and semantic resources. We are developing a verb dictionary for use in the processing of biomedical texts that includes both syntactic subcategorisation frames and semantic event frames, and links them together. In this paper, we describe the acquisition of syntactic subcategorisation frames from a large corpus of abstracts of the subject of E. Coli, together with the extraction of linguistic event frames from a subset of this corpus, in which the biological process of E. coli gene regulation has been linguistically annotated by a group of biologists. Finally, we report on work carried out to link the syntactic and semantic information together, by mapping syntactic arguments of subcategorisation frames to semantic arguments of the event frames

Domain Adaptation for Dependency Parsing at Evalita 2011

The domain adaptation task was aimed at investigating techniques for adapting state-of-the-art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target domain, namely the legal domain, represent two main novelties of the task organised at Evalita 2011. In this paper, we define the task and describe how the datasets were created from different resources. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results