The Velocity of the Propagating Wave for General Coupled Scalar Systems

We consider spatially coupled systems governed by a set of scalar density evolution equations. Such equations track the behavior of message-passing algorithms used, for example, in coding, sparse sensing, or constraint-satisfaction problems. Assuming that the "profile" describing the average state of the algorithm exhibits a solitonic wave-like behavior after initial transient iterations, we derive a formula for the propagation velocity of the wave. We illustrate the formula with two applications, namely Generalized LDPC codes and compressive sensing.Comment: 5 pages, 5 figures, submitted to the Information Theory Workshop (ITW) 2016 in Cambridge, U

Analysis of Spatially Coupled Systems using the Potential Functional with Applications to Coding Theory

For the past 70 years or so, coding theorists have been aiming at designing transmission schemes with efficient encoding and decoding algorithms that achieve the capacity of various noisy channels. It was not until the '90s that graph-based codes, such as low-density parity-check (LDPC) codes, and their associated low-complexity iterative decoding algorithms were discovered and studied in depth. Although these schemes are efficient, they are not, in general, capacity-achieving. More specifically, these codes perform well up to some algorithmic threshold on the channel parameter, which is lower than the optimal threshold. The gap between the algorithmic and optimal thresholds was finally closed by spatial coupling. In the context of coding, the belief propagation algorithm on spatially coupled codes yields capacity-achieving low-complexity transmission schemes. The reason behind the optimal performance of spatially coupled codes is ``seeding'' perfect information on the replicas at the boundaries of the coupling chain. This extra information makes decoding easier near the boundaries, and this effect is then propagated into the coupling chain upon iterations of the decoding algorithm. Spatial coupling was also applied to various other problems that are governed by low-complexity message-passing algorithms, such as random constraint satisfaction problems, compressive sensing, and statistical physics. Each system has an associated algorithmic threshold and an optimal threshold. As with coding, once the underlying graphs are spatially coupled, the algorithms for these systems exhibit optimal performance. In this thesis, we analyze the performance of iterative low-complexity message-passing algorithms on general spatially coupled systems, and we specialize our results in coding theory applications. To do this, we express the evolution of the state of the system (along iterations of the algorithm) in a variational form, in terms of the so-called potential functional, in the continuum limit approximation. This thesis consists of two parts. In the first part, we consider the dynamic phase of the message-passing algorithm, in which iterations of the algorithm modify the state of the spatially coupled system. Assuming that the boundaries of the coupled chain are appropriately ``seeded'', we find a closed-form analytical formula for the velocity with which the extra information propagates into the chain. We apply this result to coupled irregular LDPC code-ensembles with transmission over general BMS channels and to coupled general scalar systems. We perform numerical simulations for several applications and show that our formula gives values that match the empirical, observed velocity. This confirms that the continuum limit is an approximation well-suited to the derivation of the formula. In the second part of this thesis, we consider the static phase of the message-passing algorithm, when it can no longer modify the state of the system. We introduce a novel proof technique that employs displacement convexity, a mathematical tool from optimal transport, to prove that the potential functional is strictly displacement convex under an alternative structure in the space of probability measures. We hence establish the uniqueness of the state to which the spatially coupled system converges, and we characterize it. We apply this result to the (l,r)-regular Gallager ensemble with transmission over the BEC and to coupled general scalar systems

Mini-Workshop: Mathematical Analysis for Peridynamics

A mathematical analysis for peridynamics, a nonlocal elastic theory, is the subject of the mini-workshop. Peridynamics is a novel multiscale mechanical model where the canonical divergence of the stress tensor is replaced by an integral operator that sums forces at a finite distance. As such, the underlying regularity assumptions are more general, for instance, allowing discontinuous and non-differentiable displacement fields. Although the theoretical mechanical formulation of peridynamics is well understood, the mathematical and numerical analyses are in their early stages. The mini-workshop proved to be a catalyst for the emerging mathematical analyses among an international group of mathematicians

Modular-topology optimization of structures and mechanisms with free material design and clustering

Topology optimization of modular structures and mechanisms enables balancing the performance of automatically-generated individualized designs, as required by Industry 4.0, with enhanced sustainability by means of component reuse. For optimal modular design, two key questions must be answered: (i) what should the topology of individual modules be like and (ii) how should modules be arranged at the product scale? We address these challenges by proposing a bi-level sequential strategy that combines free material design, clustering techniques, and topology optimization. First, using free material optimization enhanced with post-processing for checkerboard suppression, we determine the distribution of elasticity tensors at the product scale. To extract the sought-after modular arrangement, we partition the obtained elasticity tensors with a novel deterministic clustering algorithm and interpret its outputs within Wang tiling formalism. Finally, we design interiors of individual modules by solving a single-scale topology optimization problem with the design space reduced by modular mapping, conveniently starting from an initial guess provided by free material optimization. We illustrate these developments with three benchmarks first, covering compliance minimization of modular structures, and, for the first time, the design of non-periodic compliant modular mechanisms. Furthermore, we design a set of modules reusable in an inverter and in gripper mechanisms, which ultimately pave the way towards the rational design of modular architectured (meta)materials.Comment: 30 page

Nondifferentiable energy minimization for cohesive fracture in a discontinuous Galerkin finite element framework

Until recently, most works on the computational modelling of fracture relied on a Newtonian mechanics approach, i.e., momentum balance equations describing the motion of the body along with fracture criteria describing the evolution of fractures. Robustness issues associated with this approach have been identified in the previous literature, several of which, as this thesis shows, due to the discontinuous dependence of stress field on the deformation field at the time of insertion of displacement discontinuities. Lack of continuity limits applicability of the models and undermines reliability of the numerical solutions. In particular, solutions often show non-convergent behaviour with time step refinement and exhibit nonphysical velocity fields and crack activation patterns. In addition, implicit time-stepping schemes, which are favoured in quasi-static and low-velocity problems, are challenging in such models. This is not a coincidence but a manifestation of algorithmic pitfalls of such methods. Continuity of stresses is in general hard to achieve in a computational model that employs a crack initiation criterion. Energy (variational) approaches to fracture have gained increased popularity in recent years. An energy approach has been shown to avoid introduction of a crack initiation criterion. The central idea of this model is the minimization of a mechanical energy functional, whose term representing the energy due to the cracks is a nondifferentiable function of the interface openings at zero opening displacement. A consequence of this formulation is that crack initiation happens automatically as a by-product of energy minimization. This avoids the complexities arising from the introduction of an extrinsic activation criterion but entails minimization of a nondifferentiable potential. The aim of this research is to develop robust and efficient computational algorithms for numerical implementation of the energy approach to cohesive fracture. Two computational algorithms have been proposed in a discontinuous Galerkin finite element framework, including a continuation algorithm which entails successive smooth approximations of the nondifferentiable functional and a block coordinate descent algorithm which uses generalized differential calculus for the treatment of nondifferentiability. These methods allow for a seamless transition from the uncracked to the cracked state, making possible the use of iterative solvers with implicit time-stepping, and completely sidestepping robustness issues of previous computational frameworks. A critical component of this work is validation of the robustness of the proposed numerical methods. Various numerical simulations are presented including time step and mesh size convergence studies and qualitative and quantitative comparison of simulations with experimental observations and theoretical findings. In addition, an energy-based hydro-mechanical model and computational algorithm is presented for hydraulic fracturing in impermeable media, which shows the crucial importance of continuity in multi-physics modelling. A search algorithm is developed on the basis of graph theory to identify the set of fluid-pressurized cracks among cracks in naturally fractured domains

Mechanics of Materials

All up-to-date engineering applications of advanced multi-phase materials necessitate a concurrent design of materials (including composition, processing routes, microstructures and properties) with structural components. Simulation-based material design requires an intensive interaction of solid state physics, material physics and chemistry, mathematics and information technology. Since mechanics of materials fuses many of the above fields, there is a pressing need for well founded quantitative analytical and numerical approaches to predict microstructure-process-property relationships taking into account hierarchical stationary or evolving microstructures. Owing to this hierarchy of length and time scales, novel approaches for describing/ modelling non-equilibrium material evolution with various degrees of resolution are crucial to linking solid mechanics with realistic material behavior. For example, approaches such as atomistic to continuum transitions (scale coupling), multiresolution numerics, and handshaking algorithms that pass information to models with different degrees of freedom are highly relevant in this context. Many of the topics addressed were dealt with in depth in this workshop

Kontextsensitive Modellhierarchien für Quantifizierung der höherdimensionalen Unsicherheit

We formulate four novel context-aware algorithms based on model hierarchies aimed to enable an efficient quantification of uncertainty in complex, computationally expensive problems, such as fluid-structure interaction and plasma microinstability simulations. Our results show that our algorithms are more efficient than standard approaches and that they are able to cope with the challenges of quantifying uncertainty in higher-dimensional, complex problems.Wir formulieren vier kontextsensitive Algorithmen auf der Grundlage von Modellhierarchien um eine effiziente Quantifizierung der Unsicherheit bei komplexen, rechenintensiven Problemen zu ermöglichen, wie Fluid-Struktur-Wechselwirkungs- und Plasma-Mikroinstabilitätssimulationen. Unsere Ergebnisse zeigen, dass unsere Algorithmen effizienter als Standardansätze sind und die Herausforderungen der Quantifizierung der Unsicherheit in höherdimensionalen, komplexen Problemen bewältigen können