Subdivision surface fitting to a dense mesh using ridges and umbilics

Fitting a sparse surface to approximate vast dense data is of interest for many applications: reverse engineering, recognition and compression, etc. The present work provides an approach to fit a Loop subdivision surface to a dense triangular mesh of arbitrary topology, whilst preserving and aligning the original features. The natural ridge-joined connectivity of umbilics and ridge-crossings is used as the connectivity of the control mesh for subdivision, so that the edges follow salient features on the surface. Furthermore, the chosen features and connectivity characterise the overall shape of the original mesh, since ridges capture extreme principal curvatures and ridges start and end at umbilics. A metric of Hausdorff distance including curvature vectors is proposed and implemented in a distance transform algorithm to construct the connectivity. Ridge-colour matching is introduced as a criterion for edge flipping to improve feature alignment. Several examples are provided to demonstrate the feature-preserving capability of the proposed approach

Development of an Interpretive Simulation Tool for the Proton Radiography Technique

Proton radiography is a useful diagnostic of high energy density (HED) plasmas under active theoretical and experimental development. In this paper we describe a new simulation tool that interacts realistic laser-driven point-like proton sources with three dimensional electromagnetic fields of arbitrary strength and structure and synthesizes the associated high resolution proton radiograph. The present tool's numerical approach captures all relevant physics effects, including effects related to the formation of caustics. Electromagnetic fields can be imported from PIC or hydrodynamic codes in a streamlined fashion, and a library of electromagnetic field `primitives' is also provided. This latter capability allows users to add a primitive, modify the field strength, rotate a primitive, and so on, while quickly generating a high resolution radiograph at each step. In this way, our tool enables the user to deconstruct features in a radiograph and interpret them in connection to specific underlying electromagnetic field elements. We show an example application of the tool in connection to experimental observations of the Weibel instability in counterstreaming plasmas, using $\sim 10^8$ particles generated from a realistic laser-driven point-like proton source, imaging fields which cover volumes of $\sim10$ mm$^3$. Insights derived from this application show that the tool can support understanding of HED plasmas.Comment: Figures and tables related to the Appendix are included in the published journal articl

Mixed and galerkin finite element approximation of flow in a linear viscoelastic porous medium

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2013 ElsevierThis article has been made available through the Brunel Open Access Publishing Fund.We propose two fully discrete mixed and Galerkin finite element approximations to a system of equations describing the slow flow of a slightly compressible single phase fluid in a viscoelastic porous medium. One of our schemes is the natural one for the backward Euler time discretization but, due to the viscoelasticity, seems to be stable only for small enough time steps. The other scheme contains a lagged term in the viscous stress and pressure evolution equations and this is enough to prove unconditional stability. For this lagged scheme we prove an optimal order a priori error estimate under ideal regularity assumptions and demonstrate the convergence rates by using a model problem with a manufactured solution. The model and numerical scheme that we present are a natural extension to ‘poroviscoelasticity’ of the poroelasticity equations and scheme studied by Philips and Wheeler in (for example) [Philip Joseph Philips, Mary F.Wheeler, Comput. Geosci. 11 (2007) 145–158] although — importantly — their algorithms and codes would need only minor modifications in order to include the viscous effects. The equations and algorithms presented here have application to oil reservoir simulations and also to the condition of hydrocephalus — ‘water on the brain’. An illustrative example is given demonstrating that even small viscoelastic effects can produce noticeable differences in long-time response. To the best of our knowledge this is the first time a mixed and Galerkin scheme has been analysed and implemented for viscoelastic porous media

Influence-Optimistic Local Values for Multiagent Planning --- Extended Version

Recent years have seen the development of methods for multiagent planning under uncertainty that scale to tens or even hundreds of agents. However, most of these methods either make restrictive assumptions on the problem domain, or provide approximate solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions. To allow for meaningful benchmarking through measurable quality guarantees on a very general class of problems, this paper introduces a family of influence-optimistic upper bounds for factored decentralized partially observable Markov decision processes (Dec-POMDPs) that do not have factored value functions. Intuitively, we derive bounds on very large multiagent planning problems by subdividing them in sub-problems, and at each of these sub-problems making optimistic assumptions with respect to the influence that will be exerted by the rest of the system. We numerically compare the different upper bounds and demonstrate how we can achieve a non-trivial guarantee that a heuristic solution for problems with hundreds of agents is close to optimal. Furthermore, we provide evidence that the upper bounds may improve the effectiveness of heuristic influence search, and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS 2015