Design space exploration (DSE) is a tool used to evaluate and compare designs as part of the design selection process. While evaluating every possible design in a design space is infeasible, understanding design behavior and response throughout the design space may be accomplished by evaluating a subset of designs and interpolating between them using surrogate models. Surrogate modeling is a technique that uses low-cost calculations to approximate the outcome of more computationally expensive calculations or analyses, such as finite element analysis (FEA). While surrogates make quick predictions, accuracy is not guaranteed and must be considered. This research addressed the need to improve the accuracy of surrogate predictions in order to improve DSE of structural responses. This was accomplished by performing comparative analyses of linear and nonlinear dimensionality-reduction-based radial basis function (RBF) surrogate models for emulating various FEA nodal results. A total of four dimensionality reduction methods were investigated, namely principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (ISOMAP), and locally linear embedding (LLE). These methods were used in conjunction with surrogate modeling to predict nodal stresses and coordinates of a compressor blade. The research showed that using an ISOMAP-based dual-RBF surrogate model for predicting nodal stresses decreased the estimated mean error of the surrogate by 35.7% compared to PCA. Using nonlinear dimensionality-reduction-based surrogates did not reduce surrogate error for predicting nodal coordinates. A new metric, the manifold distance ratio (MDR), was introduced to measure the nonlinearity of the data manifolds. When applied to the stress and coordinate data, the stress space was found to be more nonlinear than the coordinate space for this application. The upfront training cost of the nonlinear dimensionality-reduction-based surrogates was larger than that of their linear counterparts but small enough to remain feasible. After training, all the dual-RBF surrogates were capable of making real-time predictions. This same process was repeated for a separate application involving the nodal displacements of mode shapes obtained from a FEA modal analysis. The modal assurance criterion (MAC) calculation was used to compare the predicted mode shapes, as well as their corresponding true mode shapes obtained from FEA, to a set of reference modes. The research showed that two nonlinear techniques, namely LLE and KPCA, resulted in lower surrogate error in the more complex design spaces. Using a RBF kernel, KPCA achieved the largest average reduction in error of 13.57%. The results also showed that surrogate error was greatly affected by mode shape reversal. Four different approaches of identifying reversed mode shapes were explored, all of which resulted in varying amounts of surrogate error. Together, the methods explored in this research were shown to decrease surrogate error when performing DSE of a turbomachine compressor blade. As surrogate accuracy increases, so does the ability to correctly make engineering decisions and judgements throughout the design process. Ultimately, this will help engineers design better turbomachines.

No description available.

No description available.

Design fixation is a phenomenon experienced by professional designers and engineering design students that stifles creativity and innovation through discouraging ideational productivity, fluency and diversity. During the design idea and concept generation phase of the design process, a reliance on perceptual surface feature similarities between design artefacts increases the likelihood of design fixation leading to design duplication. Psychologists, educators and designers have become increasingly interested in creative idea generation processes that encourage innovation and entrepreneurial outcomes. However, there is a notable lack of collaborative research between psychology, education and engineering design particularly on inductive reasoning of undergraduate engineering students in higher education. The data gathered and analysed for this study provides an insight into property inference decision-making preferences and decision switching (SWITCH) patterns of engineering undergraduates under similarity-based inductive judgements [SIM] and category-based inductive judgements [CAT]. For this psychology experiment, property induction tasks were devised using abstract shapes in a triad configuration. Participants (N = 180), on an undergraduate engineering programme in London, observed a triad of shapes with a target shape more similar-looking to one of two given shapes. Factors manipulated for this experiment included category alignment, category group, property type and target shape. Despite the cognitive development and maturation stage of undergraduate engineers (adults) in higher education, this study identified similarity-based inductive judgements [SIM] to play a significant role during inductive reasoning relative to the strength of category-based inductive judgements [CAT]. In addition to revealing the property inference decision-making preferences of a sample of undergraduate engineers (N = 180), two types of switch classification and two types of non-switch classification (SWITCH) were found and named SIM_NCC, SIM-Salient, Reverse_CAT and CAT_Switching. These different classifications for property inference switching and non-switching presented a more complex pattern of decision-making driven by the relative strength between similarity-based inductive judgements [SIM] and category-based inductive judgements [CAT]. The conditions that encouraged CAT_Switching is of particular interest to design because it corresponds to inference decision switching that affirms the sharing of properties between dissimilar-looking shapes designated as category members, i.e., in a conflicting category alignment condition (CoC). For CAT_Switching, this study found a significant interaction between a particular set of conditions that significantly increased the likelihood of property inference decisions switching to affirm the sharing of properties between dissimilar-looking shapes. Stimuli conditions that combined a conflicting category alignment condition (where dissimilar-looking shapes belong to the same category) with category specificity, a causal property and a target shape with merged (or blended) perceptual surface features significantly increased the likelihood of a property inference decision switching. CAT_Switching has important implications for greater ideational productivity, fluency and diversity to discourage design fixation within the conceptual design space. CAT_Switching conditions could encourage more creative design transformations with alternative design functions through inductive inferences that generalise between dissimilar artefact designs. The findings from this study led to proposing a Cartesian view of the concept design space to represent the possibilities for greater movements through flexible and expanding category boundaries to encourage conceptual combinations, greater ideational fluency and greater ideational diversity within a configuration design space. This study has also created a platform for further research into property inference decision-making, ideational diversity and category boundary flexibility under stimuli conditions that encourage designers and design students to make inductive generalisations between dissimilar domains of knowledge through a greater emphasis on causal relations and semantic networks.

This PhD dissertation proposes innovative methodologies to support the designing and the mapping process of embedded systems. Due to the increasing requirements, embedded systems have become quite complex, as they consist of several partially dependent heterogeneous components. Systematic Design Space Exploration (DSE) methodologies are required to support the near-optimal design of embedded systems within the available short time-to-market. In this target domain, the existing DSE approaches either require too much exploration time to find near-optimal designs due to the high number of parameters and the correlations between the parameters of the target domain, or they end up with a less efficient trade-off result in order to find a design within acceptable time. In this dissertation we present an alternative DSE methodology, which is based on systematic creation of scalable and near-optimal DSE frameworks. The frameworks describe all the available options of the exploration space in a finite set of classes. A set of principles is presented which is used in the reusable DSE methodology to create a scalable and near-optimal framework and to efficiently use it to derive scalable and near-optimal design solutions within a Pareto trade-off space. The DSE reusable methodology is applied to several stages of the embedded system design flow to derive scalable and near-optimal methodologies. The first part of the dissertation is dedicated to the development of mapping methodologies for storing large embedded system data arrays in the lower layers of the on-chip background data memory hierarchy, and the second part to the DSE methodologies for the processing part of SW/HW architectures in embedded systems including the foreground memory systems. Existing mapping approaches for the background memory part are either enumerative, symbolic/polyhedral and worst case (heuristics) approximations. The enumerative approaches require too much exploration time, the worst case approximation lead to overestimation of the storage requirements, whereas the symbolic/polytope approaches are scalable and near-optimal for solid and regular iteration spaces. By applying the new reusable DSE methodology, we have developed an intra-signal in-place optimization methodology which is scalable and near-optimal for highly irregular access schemes. Scalable and near-optimal solutions for the different cases of the proposed methodology have been developed for the cases of non-overlapping and overlapping store and load access schemes. To support the proposed methodology, a new representation of the array access schemes, which is appropriate to express the irregular shapes in a scalable and near-optimal way, is presented. A general pattern formulation has been proposed which describes the access scheme in a compact and repetitive way. Pattern operations were developed to combine the patterns in a scalable and near-optimal way under all the potential pattern combination cases, which may exist in the application under study. In the processing oriented part of the dissertation, a DSE methodology is developed for mapping instance of a predefined target application domain onto a partially fixed architecture platform template, which consists of one processor core and several custom hardware accelerators. The DSE methodology consists of uni-directional steps, which are implemented through parametric templates and are applied without costly design iterations. The proposed DSE methodology explores the space by instantiating the steps and propagating design constraints which prune design options following the steps ordering. The result is a final Pareto trade-off curve with the most relevant near-optimal designs. As the scheduling and the assignment are the major tasks of both the foreground and the datapath, near-optimal and scalable techniques are required to support the parametric templates of the proposed DSE methodology. A framework which describes the scheduling and assignment of the scalars into the registers and the scheduling and assignment of the operation into the function units of the data path is developed. Based on the framework, a systematic methodology to arrive at parametric templates for scheduling and assignment techniques which satisfy the target domain constraints is developed. In this way, a scalable parametric template for scheduling and assignment tasks is created, which guarantees near-optimality for the domain under study. The developed template can be used in the Foreground Memory Management step and Data-path mapping step of the overall design flow. For the DSE of the domain under study, near-optimal results are hence achieved through a truly scalable technique. / Η παρούσα διδακτορική διατριβή προτείνει καινοτόμες μεθοδολογίες για τον σχεδιασμό και τη διαδικασία απεικόνισης σε ενσωματωμένα συστημάτα. Λόγω των αυξανόμενων απαιτήσεων, τα ενσωματωμένα συστήματα είναι αρκετά περίπλοκα, καθώς αποτελούνται από πολλά και εν μέρει εξαρτώμενα ετερογενή στοιχεία. Συστηματικές μεθοδολογίες για την εξερεύνηση του χώρου λύσεων (Design Space Exploration – DSE) απαιτούνται σχεδόν βέλτιστες σχεδιάσεις ενσωματωμένων συστημάτων εντός του διαθέσιμου χρονου. Οι υπάρχουσες DSE μεθοδολογίες απαιτούν είτε πάρα πολύ χρόνο εξερεύνησης για να βρουν τους σχεδόν βέλτιστους σχεδιασμούς, λόγω του μεγάλου αριθμού των παραμέτρων και τις συσχετίσεις μεταξύ των παραμέτρων, ή καταλήγουν με ένα λιγότερο βέλτιστο σχέδιο, προκειμένου να βρειθεί ένας σχεδιασμός εντός του διαθέσιμου χρόνου. Στην παρούσα διδακτορική διατριβή παρουσιάζουμε μια εναλλακτική DSE μεθοδολογία, η οποία βασίζεται στη συστηματική δημιουργία επεκτάσιμων και σχεδόν βέλτιστων DSE πλαισίων. Τα πλαίσια περιγράφουν όλες τις διαθέσιμες επιλογές στο χώρο εξερεύνησης με ένα πεπερασμένο σύνολο κατηγοριών. Ένα σύνολο αρχών χρησιμοποιείται στην επαναχρησιμοποιήούμενη DSE μεθοδολογία για να δημιουργήσει ένα επεκτάσιμο και σχεδόν βέλτιστο DSE πλαίσιο και να χρησιμοποιήθεί αποτελεσματικά για να δημιουργήσει επεκτάσιμες και σχεδόν βέλτιστες σχεδιαστικές λύσεις σε ένα Pareto Trade-off χώρο λύσεων. Η DSE μεθοδολογία εφαρμόζεται διάφορα στάδια της σχεδιαστικής ροής για ενσωματωμένα συστήματα και να δημιουργήσει επεκτάσιμες και σχεδόν βέλτιστες μεθοδολογίες. Το πρώτο μέρος της διατριβής είναι αφιερωμένο στην ανάπτυξη των μεθόδων απεικόνισης για την αποθήκευση μεγάλων πινάκων που χρησιμοποιούνται στα ενσωματωμένα συστήματα και αποθηκεύονται στα χαμηλότερα στρώματα της on-chip Background ιεραρχία μνήμης. Το δεύτερο μέρος είναι αφιερωμένο σε DSE μεθοδολογίες για το τμήμα επεξεργασίας σε αρχιτεκτονικές λογισμικού/υλικού σε ενσωματωμένα συστήματα, συμπεριλαμβανομένων των συστημάτων της προσκήνιας (foreground) μνήμης. Υπάρχουσες μεθοδολογίες απεικόνισης για την Background μνήμης είτε εξονυχιστικές, συμβολικές/πολυεδρικές και προσεγγίσεις με βάση τη χειρότερη περίπτωση. Οι εξονυχιστικές απαιτούν πάρα πολύ μεγάλο χρόνο εξερεύνησης, οι προσεγγίσεις οδηγούν σε υπερεκτίμηση των απαιτήσεων αποθήκευσης, ενώ οι συμβολικές είναι επεκτάσιμη και σχεδόν βέλτιστές μονο για τακτικούς χώρους επαναλήψεων. Με την εφαρμογή της προτεινόμενης DSE μεθοδολογίας αναπτύχθηκε μια επεκτάσιμη και σχεδόν βέλτιστη μεθοδολγοία για την εύρεση του αποθηκευτικού μεγέθους για τα δεδομένα ενός πίνακα για άτακτους και για τακτικούς χώρους επαναλήψεων. Προτάθηκε μια νέα αναπαράσταση των προσπελάσεων στη μνήμη, η οποία εκφράζει τα ακανόνιστα σχήματα στο χώρο επεναλήψεων με επακτάσιμο και σχεδόν βέλτιστο τρόπο. Στο δεύτερο τμήμα της διατριβής, μια DSE μεθοδολογία αναπτύχθηκε για το σχεδιασμό ενός προκαθορισμένου τομέα από εφαρμογές σε μια μερικώς αποφασισμένη αρχιτεκτονική πλατφόρμα, η οποία αποτελείται από ένα πυρήνα επεξεργαστή και αρκετούς συνεπεξεργαστές. Η DSE μεθοδολογία αποτελείται από μονής κατεύθυνσης βήματα, τα οποία υλοποιούνται μέσω παραμετρικών πλαισίων και εφαρμόζονται αποφέυγοντας τις δαπανηρές επαναλήψεις κατά τον σχεδιασμό. Η προτεινόμενη DSE μεθοδολογία εξερευνά το χώρο βρίσκοντας στιγμιότυπα για καθε βήμα και διαδίδονατς τις αποφάσεις μεταξύ βημάτων. Με αυτό το τρόπο κλαδεύουν τις επιλογές σχεδιασμού στα επόμενα βήματα. Το αποτέλεσμα είναι μια Pareto καμπύλη. Ένα DSE πλαίσιο προτάθηκε που περιγράφει τις τεχνικές χρονοπρογραμματισμού και ανάθεσης πόρων των καταχωρητών και των μονάδων εκτέλεσης του συστήματος. Προτάθηκε μια μεθοδολογία για να δημιουργεί σχεδόν βέλτιστα και επεκτάσιμα παραμετρικά πρότυπα για τον χρονοπρογραμματισμό και την ανάθεση πόρων που ικανοποιεί τους περιορισμούς ενός τομέα εφαρμογών.

Problem statement: The D-optimal design is often used in clinical research. In multi-factor clinical experiments it is natural to restrict the experiment's design space so as not to give a patient the combination of several high dose treatments simultaneously. Under such design space restrictions it is unknown what designs are D-optimal. The goal of the thesis has been to find D-optimal designs for these design spaces. Approach: Two new algorithms for finding D-optimal designs with one, two or three factors with linear models has been developed and implemented in MATLAB. Two restricted design spaces were explored. In cases when the program could not find the D-optimal design an analytic approach was used. Results: Special attention was given to the two factor model with interaction. All of the D-optimal designs for this model, N less or equal to 30, and their permutations have been listed as well as their continous designs. Conclusion: In one of the restricted design regions a simple design pattern appeared for N greater than or equal to 7. In the other restricted design region no obvious pattern was found but its continuous design could be calculated through analysis. It turned out that the number of trials at the lowest dose combination did not change when moving from the full space design to the restricted design regions. / Frågeställning: D-optimala designer är vanliga i kliniska studier. När flera faktorer (läkemedel) prövas samtidigt kan det vara nödvändigt att begränsa försöksrummet så att patienterna undviker att få en hög dos av flera faktorer samtidigt. I sådana begränsade försöksrum är det okänt vilka designer som är D-optimala. Uppsatsens mål har varit att hitta D-optimala designer i begränsade försöksrum. Metod: Två nya algoritmer för att hitta D-optimala designer med en, två eller tre dimensioner och linjära modeller har utvecklats och implementerats i MATLAB. Två begränsade försöksrum har utforskats. I de fall då MATLAB-programmet inte kunde hitta de D-optimala designerna användes analytiska metoder. Resultat: Analys av en tvåfaktormodell med interaktion utforskades särskilt noggrant. Alla D-optimala designer och permutationer av dessa i de båda begränsade försöksrummen har listats för alla N mindre än eller lika med 30, samt även deras kontinuerliga designer. Slutsats: För det ena försöksrummet upptäcktes ett mönster i designen då N är större än eller lika med 7. I det andra försöksrummet upptäcktes inget mönster och det krävdes således analytiska metoder för att finna dess kontinuerliga design. Det visade sig att antalet försök i den lägsta doskombinationen förblev oförändrat då man bytte från det fulla designrummet till de båda begränsade designrummen.

Dans le cadre industriel et académique, les méthodologies de développement logiciel exploitent de plus en plus le concept de “modèle” afin d’appréhender la complexité des systèmes temps réel critiques. En particulier, celles-ci définissent une étape dans laquelle un modèle fonctionnel, conçu comme un graphe de blocs fonctionnels communiquant via des échanges de signaux de données, est déployé sur un modèle de plateforme d’exécution matérielle et un modèle de plateforme d’exécution logicielle composé de tâches et de messages. Cette étape appelée étape de déploiement, permet d’établir une architecture opérationnelle du système nécessitant une validation des propriétés temporelles du système. Dans le contexte des systèmes temps réel dirigés par les évènements, la vérification des propriétés temporelles est réalisée à l’aide de l’analyse d’ordonnançabilité basée sur l’analyse des temps de réponse. Chaque choix de déploiement effectué a un impact essentiel sur la validité et la qualité du système. Néanmoins, les méthodologies existantes n’offrent pas de support permettant de guider le concepteur d’applications durant l’exploration de l’espace des architectures possibles. L’objectif de ces travaux de thèse consiste à mettre en place des techniques d’analyse et de synthèse automatiques permettant de guider le concepteur vers une architecture opérationnelle valide et optimisée par rapport aux performances du système. Notre proposition est dédiée à l’exploration de l’espace des architectures en tenant compte à la fois des quatre degrés de liberté déterminés durant la phase de déploiement, à savoir (j) le placement des éléments fonctionnels sur les éléments de calcul et de communication de la plateforme d’exécution, (ii) le partitionnement des éléments fonctionnels en tâches temps réel et des signaux de données en messages, (iii) l’affectation de priorités d’exécution aux tâches et aux messages du système et (iv) l’attribution du mécanisme de protection des données partagées pour les systèmes temps réel périodiques. Nous nous intéressons principalement à la satisfaction des contraintes temporelles et celles liées aux capacités des ressources de la plateforme cible. De plus, nous considérons l’optimisation des latences de bout-en-bout et la consommation mémoire. Les approches d’exploration architecturale présentées dans cette thèse sont basées sur la technique d’optimisation PLNE (programmation linéaire en nombres entiers) et concernent à la fois les applications activées périodiquement et celles dont l’activation est pilotée par les données. Contrairement à de nombreuses approches antérieures fournissant une solution partielle au problème de déploiement, les méthodes proposées considèrent l’ensemble du problème de déploiement. Les approches proposées dans cette thèse sont évaluées à l’aide d’applications génériques et industrielles. / Modern development methodologies from the industry and the academia exploit more and more the ”model” concept to address the complexity of critical real-time systems. These methodologies define a key stage in which the functional model, designed as a network of function blocks communicating through exchanged data signals, is deployed onto a hardware execution platform model and implemented in a software model consisting of a set of tasks and messages. This stage so-called deployment stage allows establishment of an operational architecture of the system, thus it requires evaluation and validation of the temporal properties of the system. In the context of event-driven real-time systems, the verification of temporal properties is performed using the schedulability analysis based on the response time analysis. Each deployment choice has an essential impact on the validity and the quality of the system. However, the existing methodologies do not provide supportto guide the designer of applications in the exploration of the operational architectures space. The objective of this thesis is to develop techniques for analysis and automatic synthesis of a valid operational architecture optimized with respect to the system performances. Our proposition is dedicated to the exploration of architectures space considering at the same time the four degrees of freedom determined during the deployment phase, (i) the placement of functional elements on the computing and communication resources of the execution platform, (ii) the partitioning of function elements into real time tasks and data signals into messages, (iii) the priority assignment to system tasks and messages and (iv) the assignment of shared data protection mechanism for periodic real-time systems. We are mainly interested in meeting temporal constraints and memory capacity of the target platform. In addition, we are focusing on the optimization of end-to-end latency and memory consumption. The design space exploration approaches presented in this thesis are based on the MILP (Mixed Integer Linear programming) optimization technique and concern at the same time time-driven and data-driven applications. Unlike many earlier approaches providing a partial solution to the deployment problem, our methods consider the whole deployment problem. The proposed approaches in this thesis are evaluated using both synthetic and industrial applications.

En la presente tesis se propone una metodología de desarrollo de estrategias híbridas para la mitigación de fallos inducidos por radiación en los sistemas empotrados modernos. La propuesta se basa en los principios del co-diseño de sistemas y consiste en la combinación selectiva, incremental y flexible de enfoques de tolerancia a fallos basados en hardware y software. Es decir, la exploración del espacio de soluciones se fundamenta en una estrategia híbrida de grano fino. El flujo de diseño está guiado por los requisitos de la aplicación. Esta metodología se ha denominado: co-endurecimiento. De esta forma, es posible diseñar sistemas embebidos confiables a bajo coste, donde no sólo se satisfagan los requisitos de confiabilidad y las restricciones de diseño, sino que también se evite el uso excesivo de costosos mecanismos de protección (hardware y software).

Modern wireless mobile communication technology has made noticeable improvements from the technologies in the past but is still plagued by poor power efficiency of power amplifiers found in today’s base stations. One of the factors that affect the power efficiency adversely comes from modern modulation techniques like orthogonal frequency division multiplexing which result in signals with high peak to average power ratio, also known as the crest factor. Crest factor reduction algorithms are used to solve this problem. However, the dominant method of hardware description for synthesis has been to start with writing register transfer level code which gives a very fixed implementation that may not be the optimal solution. This thesis project is focused on developing a peak cancellation crest factor reduction system, using a high-level language as the system design language, and synthesizing it using high-level synthesis. The aim is to find out if highlevel synthesis design methodology can yield increased productivity and improved quality of results for such designs as compared to the design methodology that requires the system to be implemented at the register transfer level. Design space exploration is performed to find an optimal design with respect to area. Finally, a few parameters are presented to measure the performance of the system, which helps in tuning it. The results of design space exploration helped in choosing the best possible implementation out of four different configurations. The final implementation that resulted from high-level synthesis had an area comparable to the previous register transfer level implementation. It was also concluded that, for this design, the high-level synthesis design methodology increased productivity and decreased design time. / Användning av högnivåsyntes för reduktion av toppfaktor Det har gjorts noterbara framsteg inom modern trådlös kommunikationsteknik för mobiltelefoni, men tekniken plågas fortfarande av dålig energieffektivitet hos förstärkarna i dagens basstationer. En faktor som påverkar energieffektiviteten negativt är om signaler har en stor skillnad mellan maximal effekt och medeleffekt. Kvoten mellan maximal effekt och medeleffekt kallas för toppfaktor, och en egenskap hos moderna moduleringstekniker, såsom ortogonal frekvensdelningsmodulering, är att de har en hög toppfaktor. Algoritmer för reducering av toppfaktor kan lösa det problemet. Den dominerande metoden för design av hårdvara är att skriva kod i ett hårdvarubeskrivande språk med abstraktionsnivån Register Transfer Level och sedan använda verktyg för att syntetisera hårdvara från koden. Resultatet är en specifik implementation som inte nödvändigtvis är den optimala lösningen. Det här examensarbetet är inriktat på att utveckla ett system för reducering av toppfaktor, baserat på algoritmen Peak Cancellation, genom att skriva kod i ett högnivåspråk och använda verktyg för högnivåsyntes för att syntetisera designen. Syftet är att ta reda på om högnivåsyntes som designmetod kan ge ökad produktivitet och ökad kvalitet, för den här typen av design, jämfört med den klassiska designmetoden med abstraktionsnivån Register Transfer Level. Verktyget för högnivåsyntes användes för att på ett effektivt sätt undersöka olika designalternativ för att optimera kretsytan. I rapporten presenteras ett antal parametrar för att mäta prestandan hos systemet, vilket ger information som kan användas för finjustering. Resultatet av undersökningen av designalternativ gjorde det möjligt att välja den bästa implementationen bland fyra olika konfigurationer. Den slutgiltiga implementationen hade en kretsyta som är jämförbar med en tidigare design som implementerats med hårdvarubeskrivande språk med abstraktionsnivån Register Transfer Level. En annan slutsats är att, för den här designen, så gav designmetoden med högnivåsyntes ökad produktivitet och minskad designtid.

[ES] La presente tesis doctoral está dedicada a estudiar, desarrollar y aplicar metodologías basadas en datos, fundamentadas en modelos estadísticos multivariantes de variables latentes, para abordar el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se pone especial énfasis en los modelos causales basados en variables latentes que utilizan tanto datos provenientes de un diseño de experimentos como, principalmente, datos provenientes del proceso de producción diario, es decir, datos históricos. La tesis está estructurada en cinco partes. La primera parte discute el paradigma del aprendizaje científico en el entorno de la Industria 4.0. Se destacan los objetivos de la tesis. Además, se presenta una descripción exhaustiva de los modelos basados en variables latentes, sobre los cuales se fundamentan las metodologías novedosas propuestas en esta tesis. En la segunda parte, se presentan las novedosas aportaciones metodológicas. En primer lugar, se muestra el potencial de PLS para analizar datos del DOE, con o sin datos faltantes. Posteriormente, el potencial de los modelos causales basados en variables latentes se centra en definir el espacio de diseño de la materia prima que proporciona garantía de calidad con un cierto nivel de confianza para los atributos críticos de calidad, junto con el desarrollo de un nuevo índice de capacidad multivariante basado en el espacio latente para clasificar y seleccionar proveedores para una materia prima particular utilizada en un proceso de fabricación. La tercera parte pretende abordar aplicaciones novedosas mediante modelos causales basados en variables latentes utilizando datos históricos. En primer lugar, se trata de su aplicación en el ámbito sanitario: la Pandemia COVID-19. En este contexto, se utiliza el uso de modelos basados en variables latentes para desarrollar una alternativa a los ensayos clínicos controlados con placebo. Luego, se utilizan modelos basados en variables latentes para optimizar procesos en el marco de aplicaciones industriales. La cuarta parte presenta una interfaz gráfica de usuario desarrollada en código Python que integra los métodos desarrollados con el objetivo de ser autoexplicativa y fácil de usar. Finalmente, la última parte discute la relevancia de esta disertación, incluyendo propuestas que merecen mayor investigación. / [CA] Aquesta tesi doctoral està dedicada a estudiar, desenvolupar i aplicar metodologies basades en dades, fonamentades en models estadístics multivariants de variables latents, per abordar el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es posa un èmfasi especial en els models causals basats en variables latents que utilitzen tant; dades provinents d'un disseny d'experiments com, principalment, dades provinents del procés de producció diari, és a dir, dades històriques. La tesi està estructurada en cinc parts. A la primera part es discuteix el paradigma de l'aprenentatge científic a l'entorn de la Indústria 4.0. Es destaquen els objectius de la tesi. A més, es presenta una descripció exhaustiva dels models basats en variables latents, sobre els quals es fonamenten les noves metodologies proposades en aquesta tesi. A la segona part, es presenten les noves aportacions metodològiques. En primer lloc, es mostra el potencial de PLS per analitzar dades del DOE, amb dades faltants o sense aquestes. Posteriorment, el potencial dels models causals basats en variables latents se centra a definir l'espai de disseny de la matèria prima que proporciona garantia de qualitat amb un cert nivell de confiança per als atributs crítics de qualitat, juntament amb el desenvolupament d'un nou índex de capacitat multivariant basat en l'espai latent per a classificar i seleccionar proveïdors per a una primera matèria particular utilitzada en un procés de fabricació. La tercera part pretén abordar aplicacions noves mitjançant models causals basats en variables latents utilitzant dades històrques. En primer lloc, es tracta de la seva aplicació a l'àmbit sanitari: la Pandèmia COVID-19. En aquest context, es fa servir l'ús de models basats en variables latents per desenvolupar una alternativa als assaigs clínics controlats amb placebo. Després s'utilitzen models basats en variables latents per optimitzar processos en el marc d'aplicacions industrials. La quarta part presenta una interfície gràfica d'usuari desenvolupada en codi Python que integra els mètodes desenvolupats amb l'objectiu de ser autoexplicativa i fàcil d'usar. Finalment, l'última part discuteix la rellevància d'aquesta dissertació, incloent-hi propostes que mereixen més investigació. / [EN] The present Ph.D. thesis is devoted to studying, developing, and applying data-driven methodologies, based on multivariate statistical models of latent variables, to address the scientific learning paradigm in the Industry 4.0 environment. Particular emphasis is placed on causal latent variable-based models using both data coming from a planned design of experiments and, mainly, data coming from the daily production process, namely happenstance data. The dissertation is structured in five parts. The first part discusses the scientific learning paradigm in the Industry 4.0 environment. The objectives of the thesis are highlighted. In addition to that, a comprehensive description of latent variable-based models is presented, on which the novel methodologies proposed in this thesis are founded. In the second part, the novel methodological contributions are presented. Firstly, the potential of PLS to analyze data from DOE, with or without missing runs is illustrated. Then, the potential of causal latent variable-based models is concentrated on defining the raw material design space providing assurance of quality with a certain confidence level for the critical to quality attributes, jointly with the development of a novel latent space-based multivariate capability index to rank and select suppliers for a particular raw material used in a manufacturing process. The third part aims to address novel applications by means of causal latent variable-based models using happenstance data. First, it concerns a health application: the Pandemic COVID-19. In this context, the use of latent variable-based models is applied to develop an alternative to placebo-controlled clinical trials. Then, latent variable-based models are used to optimize processes within the framework of industrial applications. The fourth part introduces a graphical user interface developed in Python code that integrates the developed methods with the aim of being self-explanatory and user-friendly. Finally, the last part discusses the relevance of this dissertation, including proposals that deserve further research. / Borràs Ferrís, J. (2023). Causal latent space-based models for scientific learning in Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/198993