hole˙filling˙journal Filling Holes in Triangular Meshes Using Digital Images by Curve Unfolding ∗

We propose a novel approach to automatically fill holes in triangulated models. Each hole is filled using a minimum energy surface that is obtained in three steps. First, we unfold the hole boundary onto a plane using energy minimization. Second, we triangulate the unfolded hole using a constrained Delaunay triangulation. Third, we embed the triangular mesh as a minimum energy surface in R 3. When embedding the triangular mesh, any energy function can be used to estimate the missing data. We use a variational multi-view approach to estimate the missing data. The running time of the method depends primarily on the size of the hole boundary and not on the size of the model, thereby makin

A data-driven approach to modelling structures

This thesis is focussed on machine-learning approaches to defining accurate models for structural dynamics. The work is motivated by the concept of `digital twin' and is an attempt to build tools that could be included in a modelling campaign for structures or within the context of a digital twin used for structural health monitoring (or more broadly, asset management). In recent years, machine learning has provided solutions to many modelling problems, offering solutions that do not require exact knowledge of the physics of the phenomena that are modelled. For structural dynamics this approach can be quite useful, since accurate mathematical representations of the physics of several structures are often not available. Moreover, for performing \textit{structural health monitoring} SHM of structures, data should be used, making machine learning a straightforward way to deal with such problems. The thesis attempts to exploit powerful machine learning algorithms to perform inference for structures in situations that traditional methodologies might fail. The attempts concern several fields of structural dynamics, such as population-based structural health monitoring, modelling under uncertainty and with a combination of known and unknown environmental conditions, performing modal decomposition for structures with nonlinear elements and defining the remaining useful life of a structure within a population of similar structures. The methodologies presented yield very promising results and reinforce the idea that machine learning, in some cases combined with physics, can be used as a tool to define accurate models of structures. As described in the first chapters of the thesis, an efficient modelling strategy for structures is to use various different models in order to model different parts, substructures or functionalities of a structure. Therefore, an organising technique for all the available data and models that are used is required. For this reason, an \textit{ontological approach} is proposed herein to include all the aforementioned elements and in order to facilitate knowledge sharing. After defining an organising technique for such a project, some data-driven schemes using novel machine-learning algorithms are presented. Initially, a method to define nonlinear normal modes of oscillations of structures is presented. The method is based on the use of a variation of a \textit{generative adversarial network} (GAN), and proves to provide quite efficient modal decomposition, under specific assumptions. The generative adversarial network algorithm is further explored and an algorithm is developed to define \textit{generative mirror models} of structures. The algorithm is developed to perform in an environment where both known/measured and unknown variables affect a structure. The algorithm, being a generative algorithm, provides a probability distribution of potential outcomes, rather than single-point predictions, allowing probabilistic assessment and planning about a structure to be undertaken. Moreover, \textit{population-based structural health monitoring} (PBSHM) is addressed using machine-learning algorithms. Performing inference in heterogeneous populations can be complicated, because of the big differences between structures within such populations. In the current thesis, a \textit{graph neural network} (GNN) approach is combined with the transformation of structures into graphs, to perform inference in such a population. The novel GNN algorithm proves able to learn efficiently the interaction physics between structural members and their environment. Finally, a generative model is used to deal with the problem of estimating the remaining useful life of structures within a population. This algorithm is also a variation of the GAN and is built to generate time series. Using this method, a probability density is defined over the remaining lifetime of a structure, exploiting information available from other structures of the population, for which data are available and which have reached their total lifetime. The new contribution of this research is the use of currently-state-of-the-art machine learning models for the purposes of structural dynamics. GANs are used for purposes other than their original purpose (artificial data generation), i.e. to perform nonlinear modal analysis and to define generative digital twins of structures. Such models are also used with a view to defining a generative time-series model, which is exploited to estimate the remaining useful lifetime of structures within a population. A second novel type of model that is exploited in the current thesis for the purposes of structural dynamics is that of graph neural networks, which are used to infer the normal condition characteristics of structures within a population

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm

Georisks in the Mediterranean and their mitigation

An international scientific conference organised by the Seismic Monitoring and Research Unit, Department of Geoscience, Faculty of Science, Department of Civil and Structural Engineering and Department of Construction and Property Management, Faculty of the Built Environment, University of Malta.Part of the SIMIT project: Integrated civil protection system for the Italo-Maltese cross-border area. Italia-Malta Programme – Cohesion Policy 2007-2013This conference is one of the activities organised within the SIMIT strategic project (Integrated Cross-Border Italo-Maltese System of Civil Protection), Italia-Malta Operational Programme 2007 – 2013. SIMIT aims to establish a system of collaboration in Civil Protection procedures and data management between Sicilian and Maltese partners, so as to guarantee the safety and protection of the citizens and infrastructure of the cross-border area. It is led by the Department of Civil Protection of the Sicilian region, and has as other partners the Department of Civil Protection of Malta and the Universities of Palermo, Catania and Malta. SIMIT was launched in March 2013, and will come to a close in October 2015. Ever since the initial formulation of the project, it has been recognised that a state of national preparedness and correct strategies in the face of natural hazards cannot be truly effective without a sound scientific knowledge of the hazards and related risks. The University of Malta, together with colleagues from other Universities in the project, has been contributing mostly to the gathering and application of scientific knowledge, both in earthquake hazard as well as in building vulnerability. The issue of seismic hazard in the cross-border region has been identified as deserving foremost importance. South-East Sicily in particular has suffered on more than one occasion the effects of large devastating earthquakes. Malta, although fortunately more removed from the sources of such large earthquakes, has not been completely spared of their damaging effects. The drastic increase in the building density over recent decades has raised the level of awareness and concern of citizens and authorities about our vulnerability. These considerations have spurred scientists from the cross-border region to work together towards a deeper understanding of the underlying causes and nature of seismic and associated hazards, such as landslide and tsunami. The SIMIT project has provided us with the means of improving earthquake surveillance and analysis in the Sicily Channel and further afield in the Mediterranean, as well as with facilities to study the behaviour of our rocks and buildings during earthquake shaking. The role of the civil engineering community in this endeavour cannot be overstated, and this is reflected in the incorporation, from the beginning, of the civil engineering component in the SIMIT project. Constructing safer buildings is now accepted to be the major option towards human loss mitigation during strong earthquakes, and this project has provided us with a welcome opportunity for interaction between the two disciplines. Finally the role of the Civil Protection authorities must occupy a central position, as we recognize the importance of their prevention, coordination and intervention efforts, aided by the input of the scientific community. This conference brings together a diversity of geoscientists and engineers whose collaboration is the only way forward to tackling issues and strategies for risk mitigation. Moreover we welcome the contribution of participants from farther afield than the Central Mediterranean, so that their varied experience may enhance our efforts. We are proud to host the conference in the historic city of Valletta, in the heart of the Mediterranean, which also serves as a constant reminder of the responsibility of all regions to protect and conserve our collective heritage.peer-reviewe

Proceedings of the First International Workshop on Mathematical Foundations of Computational Anatomy (MFCA'06) - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceNon-linear registration and shape analysis are well developed research topic in the medical image analysis community. There is nowadays a growing number of methods that can faithfully deal with the underlying biomechanical behaviour of intra-subject shape deformations. However, it is more difficult to relate the anatomical shape of different subjects. The goal of computational anatomy is to analyse and to statistically model this specific type of geometrical information. In the absence of any justified physical model, a natural attitude is to explore very general mathematical methods, for instance diffeomorphisms. However, working with such infinite dimensional space raises some deep computational and mathematical problems. In particular, one of the key problem is to do statistics. Likewise, modelling the variability of surfaces leads to rely on shape spaces that are much more complex than for curves. To cope with these, different methodological and computational frameworks have been proposed. The goal of the workshop was to foster interactions between researchers investigating the combination of geometry and statistics for modelling biological shape variability from image and surfaces. A special emphasis was put on theoretical developments, applications and results being welcomed as illustrations. Contributions were solicited in the following areas: * Riemannian and group theoretical methods on non-linear transformation spaces * Advanced statistics on deformations and shapes * Metrics for computational anatomy * Geometry and statistics of surfaces 26 submissions of very high quality were recieved and were reviewed by two members of the programm committee. 12 papers were finally selected for oral presentations and 8 for poster presentations. 16 of these papers are published in these proceedings, and 4 papers are published in the proceedings of MICCAI'06 (for copyright reasons, only extended abstracts are provided here)

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Index to 1983 NASA Tech Briefs, volume 8, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1983 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences