Marcos Lage

Meshless Helmholtz-Hodge Decomposition

Vector fields analysis traditionally distinguishes conservative (curl-free) from mass preserving (divergence-free) components. The Helmholtz-Hodge decomposition allows separating any vector field into the sum of three uniquely defined components: curl free, divergence free and harmonic. This decomposition is usually achieved by using mesh-based methods such as finite differences or finite elements. This work presents a new meshless approach to the Helmholtz-Hodge decomposition for the analysis of 2D discrete vector fields. It embeds into the SPH particle-based framework. The proposed method is efficient and can be applied to extract features from a 2D discrete vector field and to multiphase fluid flow simulation to ensure incompressibility.

CHF: A Scalable Topological Data Structure for Tetrahedral Meshes

This work introduces a scalable topological data structure for manifold tetrahedral meshes called Compact Half-Face (CHF). It provides a high degree of scalability, since it is able to optimize the memory consumption/execution time ratio for different applications and data by using features of its different levels. An object-oriented API using class inheritance and virtual instantiation enables a unique interface for each function at any level. CHF requires very few memory, is simple to implement and easy to use, since it substitutes pointers by container of integers and basic bit-wise rules.

Urbane: A 3D framework to support data driven decision making in urban development

Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers.

Urban Pulse: Capturing the Rhythm of Cities

Cities are inherently dynamic. Interesting patterns of behavior typically manifest at several key areas of a city over multiple temporal resolutions. Studying these patterns can greatly help a variety of experts ranging from city planners and architects to human behavioral experts. Recent technological innovations have enabled the collection of enormous amounts of data that can help in these studies. However, techniques using these data sets typically focus on understanding the data in the context of the city, thus failing to capture the dynamic aspects of the city. The goal of this work is to instead understand the city in the context of multiple urban data sets. To do so, we define the concept of an “urban pulse” which captures the spatio-temporal activity in a city across multiple temporal resolutions. The prominent pulses in a city are obtained using the topology of the data sets, and are characterized as a set of beats. The beats are then used to analyze and compare different pulses. We also design a visual exploration framework that allows users to explore the pulses within and across multiple cities under different conditions. Finally, we present three case studies carried out by experts from two different domains that demonstrate the utility of our framework.

Neighborhood grid

This paper introduces a novel and efficient data structure, called neighborhood grid, capable of supporting large number of particle based elements on GPUs (graphics processing units), and is used for optimizing fluid animation with the use of GPU computing. The presented fluid simulation approach is based on SPH (smoothed particle hydrodynamics) and uses a unique algorithm for the neighborhood gathering. The brute force approach to neighborhood gathering of n particles has complexity O ( n 2 ) , since it involves proximity queries of all pairs of fluid particles in order to compute the relevant mutual interactions. Usually, the algorithm is optimized by using spatial data structures which subdivide the environment in cells and then classify the particles among the cells based on their position, which is not efficient when a large number of particles are grouped in the same cell. Instead of using such approach, this work presents a novel and efficient data structure that maintains the particles into another form of proximity data structure, called neighborhood grid. In this structure, each cell contains only one particle and does not directly represent a discrete spatial subdivision. The neighborhood grid does process an approximate spatial neighborhood of the particles, yielding promising results for real time fluid animation, with results that goes up to 9 times speedup, when compared to traditional GPU approaches, and up to 100 times when compared against CPU implementations. We present a new data structure for the neighborhood gathering on fluid simulation, called neighborhood grid.The neighborhood grid has an expressive speedup against the uniform grid on GPUs.The neighborhood grid uses less memory when compared with the uniform grid.

A heterogeneous system based on GPU and multi-core CPU for real-time fluid and rigid body simulation

Computational fluid dynamics in simulation has become an important field not only for physics and engineering areas but also for simulation, computer graphics, virtual reality and even video game development. Many efficient models have been developed over the years, but when many contact interactions must be processed, most models present difficulties or cannot achieve real-time results when executed. The advent of parallel computing has enabled the development of many strategies for accelerating the simulations. Our work proposes a new system which uses some successful algorithms already proposed, as well as a data structure organisation based on a heterogeneous architecture using CPUs and GPUs, in order to process the simulation of the interaction of fluids and rigid bodies. This successfully results in a two-way interaction between them and their surrounding objects. As far as we know, this is the first work that presents a computational collaborative environment which makes use of two different paradigms of hardware architecture for this specific kind of problem. Since our method achieves real-time results, it is suitable for virtual reality, simulation and video game fluid simulation problems.

StatCast Dashboard: Exploration of Spatiotemporal Baseball Data

This article presents a visualization and analytics infrastructure to help query and facilitate the analysis of this new tracking data. The goal is to go beyond descriptive statistics of individual plays, allowing analysts to study diverse collections of games and game events. The StatCast Dashboard visual interface helps users query, filter, and analyze the tracking data gathered by the Major League Baseball (MLB) StatCast spatiotemporal data-tracking system. The proposed system enables the exploration of the data using a simple querying interface and a set of flexible interactive visualization tools.

IBHM: index-based data structures for 2D and 3D hybrid meshes

We propose new topological data structures for the representation of 2D and 3D hybrid meshes, i.e., meshes composed of elements of different types. Hybrid meshes are playing an increasingly important role in all fields of modeling, because elements of different types are frequently considered either because such meshes are easier to construct or because they produce better numerical results. The proposed data structures are designed to achieve a balance between their memory requirements and the time complexity necessary to answer topological queries while accepting cells (elements) of different types. Additionally these data structures are easy to implement and to operate, because they are based on integer arrays and on basic arithmetic rules. A comparison with other existing data structures regarding their memory requirements and of the time complexities for the algorithms to answer general topological queries is also presented. The comparison shows that the overhead required to accept arbitrary cell types is small.

Flows with suspended and floating particles

Abstract The evolution of the configuration of a set of particles dispersed in a flowing liquid is crucial in many applications such as sedimentation, slurry transport, rheology and structured arrays of micro- and nano-particles. Direct simulation based on what is called fictitious domain method coupled with finite element method has been used to study particulate flows and sedimentation process. Here we extend the previously proposed formulations to naturally include buoyancy force and the capillary driven attraction or repulsion of particles located at fluid interfaces. The set of differential equations is discretized using a fully implicit-fully coupled fictitious domain/finite element approach, avoiding numerical instabilities that may arise from explicit integration. The proposed formulation and implementation are validated by comparing the predictions of simple 2D flows to available numerical or analytical solutions. The method is then used to analyze the flotation of 2D particles and capillary driven aggregation at fluid interfaces.

Tuning Manifold Harmonics Filters

There are several techniques for automatic music visualization, which are included with virtually any media player. The basic ingredient of those techniques is spectral analysis of the sound, used to automatically generate parameters for procedural image generation. However, only a few music visualizations rely on 3d models. This paper proposes to use spectral mesh processing techniques, namely manifold harmonics, to produce 3d music visualization. The images are generated from 3d models by deforming an initial shape, mapping the sound frequencies to the mesh harmonics. A concise representation of such frequency mapping is proposed to permit for an animated gallery interface with genetic reproduction. Such galleries allow the user to quickly navigate between visual effects. Rendering such animated galleries in real-time is a challenging task, since it requires computing and rendering the deformed shapes at a very high rate. This paper introduces a direct GPU implementation of manifold harmonics filters, which allows to display animated gallery.