Metaheuristics for Search Problems in Genomics - New Algorithms and Applications

In this thesis we made the first steps towards the systematic application of a methodology for automatically building formal models of complex biological systems. Such a methodology could be useful also to design artificial systems possessing desirable properties such as robustness and evolvability. The approach we follow in this thesis is to manipulate formal models by means of adaptive search methods called metaheuristics. In the first part of the thesis we develop state-of-the-art hybrid metaheuristic algorithms to tackle two important problems in genomics, namely, the Haplotype Inference by parsimony and the Founder Sequence Reconstruction Problem. We compare our algorithms with other effective techniques in the literature, we show strength and limitations of our approaches to various problem formulations and, finally, we propose further enhancements that could possibly improve the performance of our algorithms and widen their applicability. In the second part, we concentrate on Boolean network (BN) models of gene regulatory networks (GRNs). We detail our automatic design methodology and apply it to four use cases which correspond to different design criteria and address some limitations of GRN modeling by BNs. Finally, we tackle the Density Classification Problem with the aim of showing the learning capabilities of BNs. Experimental evaluation of this methodology shows its efficacy in producing network that meet our design criteria. Our results, coherently to what has been found in other works, also suggest that networks manipulated by a search process exhibit a mixture of characteristics typical of different dynamical regimes.

From core rigidities to dynamic capabilities: the role of external knowledge. A multiple case study

This study focuses on the processes of change that firms undertake to overcome conditions of organizational rigidity and develop new dynamic capabilities, thanks to the contribution of external knowledge. When external contingencies highlight firms’ core rigidities, external actors can intervene in change projects, providing new competences to firms’ managers. Knowledge transfer and organizational learning processes can lead to the development of new dynamic capabilities. Existing literature does not completely explain how these processes develop and how external knowledge providers, as management consultants, influence them. Dynamic capabilities literature has become very rich in the last years; however, the models that explain how dynamic capabilities evolve are not particularly investigated. Adopting a qualitative approach, this research proposes four relevant case studies in which external actors introduce new knowledge within organizations, activating processes of change. Each case study consists of a management consulting project. Data are collected through in-depth interviews with consultants and managers. A large amount of documents supports evidences from interviews. A narrative approach is adopted to account for change processes and a synthetic approach is proposed to compare case studies along relevant dimensions. This study presents a model of capabilities evolution, supported by empirical evidence, to explain how external knowledge intervenes in capabilities evolution processes: first, external actors solve gaps between environmental demands and firms’ capabilities, changing organizational structures and routines; second, a knowledge transfer between consultants and managers leads to the creation of new ordinary capabilities; third, managers can develop new dynamic capabilities through a deliberate learning process that internalizes new tacit knowledge from consultants. After the end of the consulting project, two elements can influence the deliberate learning process: new external contingencies and changes in the perceptions about external actors.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Rich media content adaptation in e-learning systems

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning experiences and services. Another e-learning challenge is represented by m-learning (which stands for mobile learning), which is emerging as a consequence of mobile terminals diffusion and provides the opportunity to browse didactical materials everywhere, outside places that are traditionally devoted to education. Both such situations share the need to access materials in limited conditions and collide with the growing use of rich media in didactical contents, which are designed to be enjoyed without any restriction. Nowadays, Web-based teaching makes great use of multimedia technologies, ranging from Flash animations to prerecorded video-lectures. Rich media in e-learning can offer significant potential in enhancing the learning environment, through helping to increase access to education, enhance the learning experience and support multiple learning styles. Moreover, they can often be used to improve the structure of Web-based courses. These highly variegated and structured contents may significantly improve the quality and the effectiveness of educational activities for learners. For example, rich media contents allow us to describe complex concepts and process flows. Audio and video elements may be utilized to add a “human touch” to distance-learning courses. Finally, real lectures may be recorded and distributed to integrate or enrich on line materials. A confirmation of the advantages of these approaches can be seen in the exponential growth of video-lecture availability on the net, due to the ease of recording and delivering activities which take place in a traditional classroom. Furthermore, the wide use of assistive technologies for learners with disabilities injects new life into e-learning systems. E-learning allows distance and flexible educational activities, thus helping disabled learners to access resources which would otherwise present significant barriers for them. For instance, students with visual impairments have difficulties in reading traditional visual materials, deaf learners have trouble in following traditional (spoken) lectures, people with motion disabilities have problems in attending on-site programs. As already mentioned, the use of wireless technologies and pervasive computing may really enhance the educational learner experience by offering mobile e-learning services that can be accessed by handheld devices. This new paradigm of educational content distribution maximizes the benefits for learners since it enables users to overcome constraints imposed by the surrounding environment. While certainly helpful for users without disabilities, we believe that the use of newmobile technologies may also become a fundamental tool for impaired learners, since it frees them from sitting in front of a PC. In this way, educational activities can be enjoyed by all the users, without hindrance, thus increasing the social inclusion of non-typical learners. While the provision of fully accessible and portable video-lectures may be extremely useful for students, it is widely recognized that structuring and managing rich media contents for mobile learning services are complex and expensive tasks. Indeed, major difficulties originate from the basic need to provide a textual equivalent for each media resource composing a rich media Learning Object (LO). Moreover, tests need to be carried out to establish whether a given LO is fully accessible to all kinds of learners. Unfortunately, both these tasks are truly time-consuming processes, depending on the type of contents the teacher is writing and on the authoring tool he/she is using. Due to these difficulties, online LOs are often distributed as partially accessible or totally inaccessible content. Bearing this in mind, this thesis aims to discuss the key issues of a system we have developed to deliver accessible, customized or nomadic learning experiences to learners with different access needs and skills. To reduce the risk of excluding users with particular access capabilities, our system exploits Learning Objects (LOs) which are dynamically adapted and transcoded based on the specific needs of non-typical users and on the barriers that they can encounter in the environment. The basic idea is to dynamically adapt contents, by selecting them from a set of media resources packaged in SCORM-compliant LOs and stored in a self-adapting format. The system schedules and orchestrates a set of transcoding processes based on specific learner needs, so as to produce a customized LO that can be fully enjoyed by any (impaired or mobile) student.

Technology acquisition through licensing. Implications for firm strategy

Nowadays licensing practices have increased in importance and relevance driving the widespread diffusion of markets for technologies. Firms are shifting from a tactical to a strategic attitude towards licensing, addressing both business and corporate level objectives. The Open Innovation Paradigm has been embraced. Firms rely more and more on collaboration and external sourcing of knowledge. This new model of innovation requires firms to leverage on external technologies to unlock the potential of firms’ internal innovative efforts. In this context, firms’ competitive advantage depends both on their ability to recognize available opportunities inside and outside their boundaries and on their readiness to exploit them in order to fuel their innovation process dynamically. Licensing is one of the ways available to firm to ripe the advantages associated to an open attitude in technology strategy. From the licensee’s point view this implies challenging the so-called not-invented-here syndrome, affecting the more traditional firms that emphasize the myth of internal research and development supremacy. This also entails understanding the so-called cognitive constraints affecting the perfect functioning of markets for technologies that are associated to the costs for the assimilation, integration and exploitation of external knowledge by recipient firms. My thesis aimed at shedding light on new interesting issues associated to in-licensing activities that have been neglected by the literature on licensing and markets for technologies. The reason for this gap is associated to the “perspective bias” affecting the works within this stream of research. With very few notable exceptions, they have been generally concerned with the investigation of the so-called licensing dilemma of the licensor – whether to license out or to internally exploit the in-house developed technologies, while neglecting the licensee’s perspective. In my opinion, this has left rooms for improving the understanding of the determinants and conditions affecting licensing-in practices. From the licensee’s viewpoint, the licensing strategy deals with the search, integration, assimilation, exploitation of external technologies. As such it lies at the very hearth of firm’s technology strategy. Improving our understanding of this strategy is thus required to assess the full implications of in-licensing decisions as they shape firms’ innovation patterns and technological capabilities evolution. It also allow for understanding the so-called cognitive constraints associated to the not-invented-here syndrome. In recognition of that, the aim of my work is to contribute to the theoretical and empirical literature explaining the determinants of the licensee’s behavior, by providing a comprehensive theoretical framework as well as ad-hoc conceptual tools to understand and overcome frictions and to ease the achievement of satisfactory technology transfer agreements in the marketplace. Aiming at this, I investigate licensing-in in three different fashions developed in three research papers. In the first work, I investigate the links between licensing and the patterns of firms’ technological search diversification according to the framework of references of the Search literature, Resource-based Theory and the theory of general purpose technologies. In the second paper - that continues where the first one left off – I analyze the new concept of learning-bylicensing, in terms of development of new knowledge inside the licensee firms (e.g. new patents) some years after the acquisition of the license, according to the Dynamic Capabilities perspective. Finally, in the third study, Ideal with the determinants of the remuneration structure of patent licenses (form and amount), and in particular on the role of the upfront fee from the licensee’s perspective. Aiming at this, I combine the insights of two theoretical approaches: agency and real options theory.

Some issues concerning the dynamic response and damage of composite laminates subjected to low velocity impact

Abstract. This thesis presents a discussion on a few specific topics regarding the low velocity impact behaviour of laminated composites. These topics were chosen because of their significance as well as the relatively limited attention received so far by the scientific community. The first issue considered is the comparison between the effects induced by a low velocity impact and by a quasi-static indentation experimental test. An analysis of both test conditions is presented, based on the results of experiments carried out on carbon fibre laminates and on numerical computations by a finite element model. It is shown that both quasi-static and dynamic tests led to qualitatively similar failure patterns; three characteristic contact force thresholds, corresponding to the main steps of damage progression, were identified and found to be equal for impact and indentation. On the other hand, an equal energy absorption resulted in a larger delaminated area in quasi-static than in dynamic tests, while the maximum displacement of the impactor (or indentor) was higher in the case of impact, suggesting a probably more severe fibre damage than in indentation. Secondly, the effect of different specimen dimensions and boundary conditions on its impact response was examined. Experimental testing showed that the relationships of delaminated area with two significant impact parameters, the absorbed energy and the maximum contact force, did not depend on the in-plane dimensions and on the support condition of the coupons. The possibility of predicting, by means of a simplified numerical computation, the occurrence of delaminations during a specific impact event is also discussed. A study about the compressive behaviour of impact damaged laminates is also presented. Unlike most of the contributions available about this subject, the results of compression after impact tests on thin laminates are described in which the global specimen buckling was not prevented. Two different quasi-isotropic stacking sequences, as well as two specimen geometries, were considered. It is shown that in the case of rectangular coupons the lay-up can significantly affect the damage induced by impact. Different buckling shapes were observed in laminates with different stacking sequences, in agreement with the results of numerical analysis. In addition, the experiments showed that impact damage can alter the buckling mode of the laminates in certain situations, whereas it did not affect the compressive strength in every case, depending on the buckling shape. Some considerations about the significance of the test method employed are also proposed. Finally, a comprehensive study is presented regarding the influence of pre-existing in-plane loads on the impact response of laminates. Impact events in several conditions, including both tensile and compressive preloads, both uniaxial and biaxial, were analysed by means of numerical finite element simulations; the case of laminates impacted in postbuckling conditions was also considered. The study focused on how the effect of preload varies with the span-to-thickness ratio of the specimen, which was found to be a key parameter. It is shown that a tensile preload has the strongest effect on the peak stresses at low span-to-thickness ratios, leading to a reduction of the minimum impact energy required to initiate damage, whereas this effect tends to disappear as the span-to-thickness ratio increases. On the other hand, a compression preload exhibits the most detrimental effects at medium span-to-thickness ratios, at which the laminate compressive strength and the critical instability load are close to each other, while the influence of preload can be negligible for thin plates or even beneficial for very thick plates. The possibility to obtain a better explanation of the experimental results described in the literature, in view of the present findings, is highlighted. Throughout the thesis the capabilities and limitations of the finite element model, which was implemented in an in-house program, are discussed. The program did not include any damage model of the material. It is shown that, although this kind of analysis can yield accurate results as long as damage has little effect on the overall mechanical properties of a laminate, it can be helpful in explaining some phenomena and also in distinguishing between what can be modelled without taking into account the material degradation and what requires an appropriate simulation of damage. Sommario. Questa tesi presenta una discussione su alcune tematiche specifiche riguardanti il comportamento dei compositi laminati soggetti ad impatto a bassa velocità. Tali tematiche sono state scelte per la loro importanza, oltre che per l’attenzione relativamente limitata ricevuta finora dalla comunità scientifica. La prima delle problematiche considerate è il confronto fra gli effetti prodotti da una prova sperimentale di impatto a bassa velocità e da una prova di indentazione quasi statica. Viene presentata un’analisi di entrambe le condizioni di prova, basata sui risultati di esperimenti condotti su laminati in fibra di carbonio e su calcoli numerici svolti con un modello ad elementi finiti. È mostrato che sia le prove quasi statiche sia quelle dinamiche portano a un danneggiamento con caratteristiche qualitativamente simili; tre valori di soglia caratteristici della forza di contatto, corrispondenti alle fasi principali di progressione del danno, sono stati individuati e stimati uguali per impatto e indentazione. D’altro canto lo stesso assorbimento di energia ha portato ad un’area delaminata maggiore nelle prove statiche rispetto a quelle dinamiche, mentre il massimo spostamento dell’impattatore (o indentatore) è risultato maggiore nel caso dell’impatto, indicando la probabilità di un danneggiamento delle fibre più severo rispetto al caso dell’indentazione. In secondo luogo è stato esaminato l’effetto di diverse dimensioni del provino e diverse condizioni al contorno sulla sua risposta all’impatto. Le prove sperimentali hanno mostrato che le relazioni fra l’area delaminata e due parametri di impatto significativi, l’energia assorbita e la massima forza di contatto, non dipendono dalle dimensioni nel piano dei provini e dalle loro condizioni di supporto. Viene anche discussa la possibilità di prevedere, per mezzo di un calcolo numerico semplificato, il verificarsi di delaminazioni durante un determinato caso di impatto. È presentato anche uno studio sul comportamento a compressione di laminati danneggiati da impatto. Diversamente della maggior parte della letteratura disponibile su questo argomento, vengono qui descritti i risultati di prove di compressione dopo impatto su laminati sottili durante le quali l’instabilità elastica globale dei provini non è stata impedita. Sono state considerate due differenti sequenze di laminazione quasi isotrope, oltre a due geometrie per i provini. Viene mostrato come nel caso di provini rettangolari la sequenza di laminazione possa influenzare sensibilmente il danno prodotto dall’impatto. Due diversi tipi di deformate in condizioni di instabilità sono stati osservati per laminati con diversa laminazione, in accordo con i risultati dell’analisi numerica. Gli esperimenti hanno mostrato inoltre che in certe situazioni il danno da impatto può alterare la deformata che il laminato assume in seguito ad instabilità; d’altra parte tale danno non ha sempre influenzato la resistenza a compressione, a seconda della deformata. Vengono proposte anche alcune considerazioni sulla significatività del metodo di prova utilizzato. Infine viene presentato uno studio esaustivo riguardo all’influenza di carichi membranali preesistenti sulla risposta all’impatto dei laminati. Sono stati analizzati con simulazioni numeriche ad elementi finiti casi di impatto in diverse condizioni di precarico, sia di trazione sia di compressione, sia monoassiali sia biassiali; è stato preso in considerazione anche il caso di laminati impattati in condizioni di postbuckling. Lo studio si è concentrato in particolare sulla dipendenza degli effetti del precarico dal rapporto larghezza-spessore del provino, che si è rivelato un parametro fondamentale. Viene illustrato che un precarico di trazione ha l’effetto più marcato sulle massime tensioni per bassi rapporti larghezza-spessore, portando ad una riduzione della minima energia di impatto necessaria per innescare il danneggiamento, mentre questo effetto tende a scomparire all’aumentare di tale rapporto. Il precarico di compressione evidenzia invece gli effetti più deleteri a rapporti larghezza-spessore intermedi, ai quali la resistenza a compressione del laminato e il suo carico critico di instabilità sono paragonabili, mentre l’influenza del precarico può essere trascurabile per piastre sottili o addirittura benefica per piastre molto spesse. Viene evidenziata la possibilità di trovare una spiegazione più soddisfacente dei risultati sperimentali riportati in letteratura, alla luce del presente contributo. Nel corso della tesi vengono anche discussi le potenzialità ed i limiti del modello ad elementi finiti utilizzato, che è stato implementato in un programma scritto in proprio. Il programma non comprende alcuna modellazione del danneggiamento del materiale. Viene però spiegato come, nonostante questo tipo di analisi possa portare a risultati accurati soltanto finché il danno ha scarsi effetti sulle proprietà meccaniche d’insieme del laminato, esso possa essere utile per spiegare alcuni fenomeni, oltre che per distinguere fra ciò che si può riprodurre senza tenere conto del degrado del materiale e ciò che invece richiede una simulazione adeguata del danneggiamento.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Technologies for urban and rural internet of things

Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Decoding complex flow phenomena via Dynamic Mode Decomposition

In this thesis, the viability of the Dynamic Mode Decomposition (DMD) as a technique to analyze and model complex dynamic real-world systems is presented. This method derives, directly from data, computationally efficient reduced-order models (ROMs) which can replace too onerous or unavailable high-fidelity physics-based models. Optimizations and extensions to the standard implementation of the methodology are proposed, investigating diverse case studies related to the decoding of complex flow phenomena. The flexibility of this data-driven technique allows its application to high-fidelity fluid dynamics simulations, as well as time series of real systems observations. The resulting ROMs are tested against two tasks: (i) reduction of the storage requirements of high-fidelity simulations or observations; (ii) interpolation and extrapolation of missing data. The capabilities of DMD can also be exploited to alleviate the cost of onerous studies that require many simulations, such as uncertainty quantification analysis, especially when dealing with complex high-dimensional systems. In this context, a novel approach to address parameter variability issues when modeling systems with space and time-variant response is proposed. Specifically, DMD is merged with another model-reduction technique, namely the Polynomial Chaos Expansion, for uncertainty quantification purposes. Useful guidelines for DMD deployment result from the study, together with the demonstration of its potential to ease diagnosis and scenario analysis when complex flow processes are involved.