Damage mechanisms in the dynamic fracture of nominally brittle polymers

Linear Elastic Fracture Mechanics (LEFM) provides a consistent framework to evaluate quantitatively the energy flux released to the tip of a growing crack. Still, the way in which the crack selects its velocity in response to this energy flux remains far from completely understood. To uncover the underlying mechanisms, we experimentally studied damage and dissipation processes that develop during the dynamic failure of polymethylmethacrylate (PMMA), classically considered as the archetype of brittle amorphous materials. We evidenced a well-defined critical velocity along which failure switches from nominally-brittle to quasi-brittle, where crack propagation goes hand in hand with the nucleation and growth of microcracks. Via post-mortem analysis of the fracture surfaces, we were able to reconstruct the complete spatiotemporal microcracking dynamics with micrometer/nanosecond resolution. We demonstrated that the true local propagation speed of individual crack fronts is limited to a fairly low value, which can be much smaller than the apparent speed measured at the continuum-level scale. By coalescing with the main front, microcracks boost the macroscale velocity through an acceleration factor of geometrical origin. We discuss the key role of damage-related internal variables in the selection of macroscale fracture dynamics.Comment: 18 pages, 21 figures, to appear in International Journal of Fractur

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Incorporating 2D tree-ring data in 3D laser scans of coarse-root systems

In times of global change biomass calculations and the carbon cycle is gaining in importance. Forests act as carbon sinks and hence, play a crucial role in worlds and forests carbon budgets. Unfortunately, growth models and biomass calculations existing so far mainly concentrate on the above-ground part of trees. For this reason, the aim of the present study is to develop an annually resolved 3D growth model for tree roots, which allows for reliable biomass calculations and can later be combined with above-ground models. A FARO scan arm was used to measure the surface of a tree-root segment. In addition, ring-width measurements were performed manually on sampled cross sections using WinDENDRO. The main goal of this study is to model root growth on an annual scale by combining these data sets. In particular, a laser scan arm was tested as a device for the realistic reproduction of tree-root architecture, although the first evaluation has been performed for a root segment rather than for an entire root system. Deviations in volume calculations differed between 5% and 7% from the actual volume and varied depending on the used modeling technique. The model with the smallest deviations represented the structure of the root segment in a realistic way and distances and diameter of cross sections were acceptable approximations of the real values. However, the volume calculations varied depending on object complexity, modeling technique and order of modeling steps. In addition, it was possible to merge tree-ring borders as coordinates into the surface model and receive age information in connection with the spatial allocation. The scan arm was evaluated as an innovative and applicable device with high potential for root modeling. Nevertheless, there are still many problems connected with the scanning technique which have an influence on the accuracy of the model but are expected to improve with technical progres

Tomographic Inverse Problems: Theory and Applications

This was the tenth Oberwolfach conference on the mathematics of tomography. The field rests on the interplay between the theoretical and applied; practical questions lead to new mathematics and pure mathematics motivates new algorithms. This workshop encompassed classical areas such as X-ray computed tomography (CT) as well as new modalities and applications such as dynamic imaging, Compton scattering tomography, hybrid imaging, optical tomography or multi-energy CT and addressed inter alia the use of methods from machine learning

Bridging the gap between reconstruction and synthesis

Aplicat embargament des de la data de defensa fins el 15 de gener de 20223D reconstruction and image synthesis are two of the main pillars in computer vision. Early works focused on simple tasks such as multi-view reconstruction and texture synthesis. With the spur of Deep Learning, the field has rapidly progressed, making it possible to achieve more complex and high level tasks. For example, the 3D reconstruction results of traditional multi-view approaches are currently obtained with single view methods. Similarly, early pattern based texture synthesis works have resulted in techniques that allow generating novel high-resolution images. In this thesis we have developed a hierarchy of tools that cover all these range of problems, lying at the intersection of computer vision, graphics and machine learning. We tackle the problem of 3D reconstruction and synthesis in the wild. Importantly, we advocate for a paradigm in which not everything should be learned. Instead of applying Deep Learning naively we propose novel representations, layers and architectures that directly embed prior 3D geometric knowledge for the task of 3D reconstruction and synthesis. We apply these techniques to problems including scene/person reconstruction and photo-realistic rendering. We first address methods to reconstruct a scene and the clothed people in it while estimating the camera position. Then, we tackle image and video synthesis for clothed people in the wild. Finally, we bridge the gap between reconstruction and synthesis under the umbrella of a unique novel formulation. Extensive experiments conducted along this thesis show that the proposed techniques improve the performance of Deep Learning models in terms of the quality of the reconstructed 3D shapes / synthesised images, while reducing the amount of supervision and training data required to train them. In summary, we provide a variety of low, mid and high level algorithms that can be used to incorporate prior knowledge into different stages of the Deep Learning pipeline and improve performance in tasks of 3D reconstruction and image synthesis.La reconstrucció 3D i la síntesi d'imatges són dos dels pilars fonamentals en visió per computador. Els estudis previs es centren en tasques senzilles com la reconstrucció amb informació multi-càmera i la síntesi de textures. Amb l'aparició del "Deep Learning", aquest camp ha progressat ràpidament, fent possible assolir tasques molt més complexes. Per exemple, per obtenir una reconstrucció 3D, tradicionalment s'utilitzaven mètodes multi-càmera, en canvi ara, es poden obtenir a partir d'una sola imatge. De la mateixa manera, els primers treballs de síntesi de textures basats en patrons han donat lloc a tècniques que permeten generar noves imatges completes en alta resolució. En aquesta tesi, hem desenvolupat una sèrie d'eines que cobreixen tot aquest ventall de problemes, situats en la intersecció entre la visió per computador, els gràfics i l'aprenentatge automàtic. Abordem el problema de la reconstrucció i la síntesi 3D en el món real. És important destacar que defensem un paradigma on no tot s'ha d'aprendre. Enlloc d'aplicar el "Deep Learning" de forma naïve, proposem representacions novedoses i arquitectures que incorporen directament els coneixements geomètrics ja existents per a aconseguir la reconstrucció 3D i la síntesi d'imatges. Nosaltres apliquem aquestes tècniques a problemes com ara la reconstrucció d'escenes/persones i a la renderització d'imatges fotorealistes. Primer abordem els mètodes per reconstruir una escena, les persones vestides que hi ha i la posició de la càmera. A continuació, abordem la síntesi d'imatges i vídeos de persones vestides en situacions quotidianes. I finalment, aconseguim, a través d'una nova formulació única, connectar la reconstrucció amb la síntesi. Els experiments realitzats al llarg d'aquesta tesi demostren que les tècniques proposades milloren el rendiment dels models de "Deepp Learning" pel que fa a la qualitat de les reconstruccions i les imatges sintetitzades alhora que redueixen la quantitat de dades necessàries per entrenar-los. En resum, proporcionem una varietat d'algoritmes de baix, mitjà i alt nivell que es poden utilitzar per incorporar els coneixements previs a les diferents etapes del "Deep Learning" i millorar el rendiment en tasques de reconstrucció 3D i síntesi d'imatges.Postprint (published version

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9