Organizational Unit:

School of Computational Science and Engineering

Permanent Link

https://hdl.handle.net/1853/70780

Parent Organization

Organizational Unit

College of Computing

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/1111

Full item page

Publication Search Results

Now showing 1 - 10 of 11

Geometric feature extraction in support of the single digital thread approach to detailed design

(Georgia Institute of Technology, 2016-12-08) Gharbi, Aroua

Aircraft design is a multi-disciplinary and complicated process that takes a long time and requires a large number of trade-offs between customer requirements, various types of constraints and market competition. Particularly detailed design is the phase that takes most of the time due to the high number of iterations between the component design and the structural analysis that need to be run before reaching an optimal design. In this thesis, an innovative approach for detailed design is suggested. It promotes a collaborative framework in which knowledge from the small scale level of components is shared and transferred to the subsystems and systems level leading to more robust and real time decisions that speed up the design time. This approach is called the Single Digital Thread Approach to Detailed Design or shortly STAnDD. The implementation of this approach is laid over a bottom-up plan, starting from the component level up to the aircraft level. In the component level and from a detailed design perspective, three major operations need to be executed in order to deploy the Single Digital Thread approach. The first one is the automatic geometric extraction of component features from a solid with no design history, the second phase is building an optimizer around the design and analysis iterations and the third one is the automatic update of the solid. This thesis suggests a methodology to implement the first phase. Extracting geometric features automatically from a solid with no history(also called dumb solid) is not an easy process especially in aircraft industry where most of the components have very complex shapes. Innovative techniques from Machine Learning were used allowing a consistent and robust extraction of the data.
Voxel-based offsetting at high resolution with tunable speed and precision using hybrid dynamic trees

(Georgia Institute of Technology, 2016-11-11) Hossain, Mohammad Moazzem

In the recent years, digital manufacturing has experienced the wave of rapid prototyping through the innovation and ubiquity in 3D printing technology. While such advancement liberates the constraints of shape selection in physical objects, 3D printing is yet to match the precision, robustness and vast applicability offered by the classical subtractive manufacturing process. To simplify the toolpath planning in conventional multi-axis CNC machining, recent researches have proposed adopting a voxel-based geometric modeling. Inherently, a voxel representation is amenable for parallel acceleration on modern ubiquitous GPU hardware. While there can be many different approaches to represent voxels, this work is based on a novel voxel data structure called hybrid dynamic tree (HDT) that combines dense grid and sparse octree in such a way that makes it both more compact (i.e., storage efficient) and better-suited to GPUs (i.e., computation effective) than state-of-the-art alternatives. This dissertation contributes in the following four aspects: First, we present a parallel method to construct the HDT representation on GPU for a CAD input modeled in a triangle mesh. In addition, to optimize the memory footprint in the HDT our research explores the theoretical storage analysis for different active node branchings in the Octree. Thus, we incorporate tunability into the HDT organization to study the complexity of memory footprint. The developed theoretical storage analysis is validated with rigorous experimentation that helps devising optimal parameter selections for storage-compact HDT representation. Next, the thesis presents a mathematical morphology based offsetting algorithm using the HDT voxel representation. At our target resolution of 4096 x 4096 x 4096, our goal is to compute large-scale offsets in minutes, match or beat the number of bits of the representation compared to state-of-the-art alternatives, and experimentally characterize any trade-offs among speed, storage, and precision. While using the HDT as the underlying data structure leads naturally to a storage-efficient representation, the challenge in developing a high-performance implementation of offset algorithm is choosing an optimal configuration of the HDT parameters.These parameters not only govern the memory footprint of the voxelized representation of the solid, but also control the parallel code execution efficiency on parallel computing units on GPU. Capability of fine-tuning of a data structure is crucial for understanding, and thereby optimizing, the developed computation-intensive algorithm that uses the HDT as the underlying voxel representation. Towards that end, this thesis explores different practical approaches to achieve high-performance voxel offsetting. First, we study the impact of the different HDT configurations on the voxel offsetting. Next, to devise a fast voxel offsetting we analyze the trade-offs between speed and accuracy through controllable size of the morphological filter. We study the impact of the decomposition of a large offset distance into a series of offsetting with smaller distances. To facilitate this trade-off analysis, we implement a GPU-accelerated error measurement technique. Finally, to enable even faster voxel offsetting, we present the principles of offloading the offset computation in the HDTs across a cluster of GPUs co-hosted on the same computing node. Our research studies the impact of different approaches for CUDA kernel execution controlled through either single or multiple independent CPU threads. In addition, we examine different load distribution policies that consider the computational disparity in the deployed GPUs. With more and more GPUs integrated on a single computing node, such exploration of algorithmic speedup through load-balanced implementation of voxel offsetting across multiple GPUs emphasizes the high scalability of the HDT's hybrid voxel representation.
Process-structure linkages with materials knowledge systems

(Georgia Institute of Technology, 2016-09-09) Brough, David

The search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due to the hierarchical nature of material structure. Yet, this problem is a key component to materials design. Customized materials data analytics provides a new avenue of research in the efforts to address the challenge described above, while accounting for the inherently stochastic nature of the available data. The analytics mine and curate transferable, high value, materials knowledge at multiple length and time scales. More specifically, this materials knowledge is cast in the form of Process-Structure-Property (PSP) linkages of interest to the design/manufacturing experts. The extension of the novel Materials Knowledge Systems (MKS) framework to Process-Structure linkages holds the exciting potential to development full PSP linkages that can be can be leveraged by experts in data science, manufacturing and materials science and engineering communities. PSP linkages are an essential component in the to realize a modern accelerated materials innovation ecosystem. This work describes the methodologies used to extend the MKS framework to Process-Structure linkages and demonstrates their utility.
Simulations of binary black holes in scalar field cosmologies

(Georgia Institute of Technology, 2016-08-01) Tallaksen, Katharine Christina

Numerical relativity allows us to solve Einstein's equations and study astrophysical phenomena we may not be able to observe directly, such as the very early universe. In this work, we examine the effect of scalar field cosmologies on binary black hole systems. These scalar field cosmologies were studied using cosmological bubbles, spherically symmetric structures that may have powered inflationary phase transitions. The Einstein Toolkit and Maya, developed at Georgia Tech, were used to simulate these systems. Systems studied include cosmological bubbles, binary black holes in vacuum, and binary black holes embedded within cosmological bubbles. Differences in mass accretion, merger trajectories, and characteristic gravitational waveforms will be presented for these systems. In the future, analyzing the parameter space of these waveforms may present a method to discover a gravitational wave signature characteristic to these systems and possibly detectable by the Laser Interferometer Gravitational-Wave Observatory.
Observing the Change in Southeastern Species’ Habitat Areas Due to Climate Change

(Georgia Institute of Technology, 2016-07-18) Bach, Renee

The southeast United States is a critical place to study the effects of climate change on biodiversity because it contains the highest richness of plants and amphibians in the contiguous U.S. and has high levels of habitat fragmentation, limiting the abilities of these diverse fauna to track their habitats. We characterize the species distributions and species richness across the regions in current conditions and in the future under different climate scenarios. Our study examines ~300 vertebrate species that live in the southeastern U.S. including birds with limited dispersal abilities, mammals, reptiles, and amphibians. We identify the biodiversity hotspots today and in the future, investigate the current and future representation of species in protected areas in the Southeast, and identify potential areas of high conservation priority with respect to future range shifts due to climate change. We develop a methodological framework that starts with raw occurrence data from GBIF, uses careful subsampling approaches, Maxent distribution modeling based on climate covariates, and combines this with several ensembles of climate projections from the present to 2070. Within this framework, we extrapolate a consensus model given the suite of projected distributions. We identify which species will be most at risk of extinction, which will require movement connectivity to track their niches, and which will interact with urbanized areas.
Agglomerative clustering for community detection in dynamic graphs

(Georgia Institute of Technology, 2016-05-10) Godbole, Pushkar J.

Agglomerative Clustering techniques work by recursively merging graph vertices into communities, to maximize a clustering quality metric. The metric of Modularity coined by Newman and Girvan, measures the cluster quality based on the premise that, a cluster has collections of vertices more strongly connected internally than would occur from random chance. Various fast and efficient algorithms for community detection based on modularity maximization have been developed for static graphs. However, since many (contemporary) networks are not static but rather evolve over time, the static approaches are rendered inappropriate for clustering of dynamic graphs. Modularity optimization in changing graphs is a relatively new field that entails the need to develop efficient algorithms for detection and maintenance of a community structure while minimizing the “Size of change” and computational effort. The objective of this work was to develop an efficient dynamic agglomerative clustering algorithm that attempts to maximize modularity while minimizing the “size of change” in the transitioning community structure. First we briefly discuss the previous memoryless dynamic reagglomeration approach with localized vertex freeing and illustrate its performance and limitations. Then we describe the new backtracking algorithm followed by its performance results and observations. In experimental analysis of both typical and pathological cases, we evaluate and justify various backtracking and agglomeration strategies in context of the graph structure and incoming stream topologies. Evaluation of the algorithm on social network datasets, including Facebook (SNAP) and PGP Giant Component networks shows significantly improved performance over its conventional static counterpart in terms of execution time, Modularity and Size of Change.
Modeling, learning, and inference of high-dimensional asynchronous event data

(Georgia Institute of Technology, 2016-05-05) Du, Nan

The increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently govern these seemly ``random'' data. In our work, we propose a unified probabilistic framework based on multivariate point processes to better predict `who will do what by when and where?' in the future. This framework comprises a systematic paradigm for modeling, learning, and making inference of large-scale asynchronous high-dimensional event data. With this common framework, we contribute in the following three aspects. Accurate Modeling: we first propose non-parametric and topic-modulated multivariate terminating point processes to capture continuous-time heterogeneous information diffusions. We then develop the low-rank Hawkes process to describe the recurrent temporal interactions among different types of entities. We also build a link between the recurrent neural network and the temporal point process to learn a general representation of the influence from the past event history. Finally, we establish a previously unexplored connection between Bayesian Nonparametrics and temporal point processes to jointly model the temporal data and other type of additional information. Efficient Learning: we develop a robust structure learning algorithm via group lasso, which is able to efficiently uncover sparse heterogeneous interdependent relations specified via vectorized parameters among the dimensions. We also propose an efficient nonnegative matrix rank minimization algorithm, which elegantly inherits the advantages from both the proximal methods and the conditional gradient methods to solve the matrix rank minimization problem under different constraints. Finally, in the data streaming setting, we develop a Bayesian inference algorithm for inferring latent variables and updating the respective model parameters based on both temporal and textual information, which achieves almost constant processing time per data sample. Scalable Inference: another important aspect of our research is to make future predictions by exploiting the learned models. Specifically, based on the terminating processes, we develop the first scalable influence estimation algorithm in continuous-time diffusion networks with provable performance guarantees. Based on the low-rank Hawkes processes, we develop the first time-sensitive recommendation algorithm, which not only can recommend the most relevant item specific to a given moment, but also can predict the next returning time for a user to a designated service. Finally, based on the recurrent point processes, we have derived an analytic solution to shape the overall network activities of users. We show that our method can provide fine-grained control over user activities in a time-sensitive fashion.
Implementation and analysis of a parallel vertex-centered finite element segmental refinement multigrid solver

(Georgia Institute of Technology, 2016-04-28) Henneking, Stefan

In a parallel vertex-centered finite element multigrid solver, segmental refinement can be used to avoid all inter-process communication on the fine grids. While domain decomposition methods generally require coupled subdomain processing for the numerical solution to a nonlinear elliptic boundary value problem, segmental refinement exploits that subdomains are almost decoupled with respect to high-frequency error components. This allows to perform multigrid with fully decoupled subdomains on the fine grids, which was proposed as a sequential low-storage algorithm by Brandt in the 1970s, and as a parallel algorithm by Brandt and Diskin in 1994. Adams published the first numerical results from a multilevel segmental refinement solver in 2014, confirming the asymptotic exactness of the scheme for a cell-centered finite volume implementation. We continue Brandt’s and Adams’ research by experimentally investigating the scheme’s accuracy with a vertex-centered finite element segmental refinement solver. We confirm that full multigrid accuracy can be preserved for a few segmental refinement levels, although we observe a different dependency on the segmental refinement parameter space. We show that various strategies for the grid transfers between the finest conventional multigrid level and the segmental refinement subdomains affect the solver accuracy. Scaling results are reported for a Cray XC30 with up to 4096 cores.
Scalable and distributed constrained low rank approximations

(Georgia Institute of Technology, 2016-04-15) Kannan, Ramakrishnan

Low rank approximation is the problem of finding two low rank factors W and H such that the rank(WH) << rank(A) and A ≈ WH. These low rank factors W and H can be constrained for meaningful physical interpretation and referred as Constrained Low Rank Approximation (CLRA). Like most of the constrained optimization problem, performing CLRA can be computationally expensive than its unconstrained counterpart. A widely used CLRA is the Non-negative Matrix Factorization (NMF) which enforces non-negativity constraints in each of its low rank factors W and H. In this thesis, I focus on scalable/distributed CLRA algorithms for constraints such as boundedness and non-negativity for large real world matrices that includes text, High Definition (HD) video, social networks and recommender systems. First, I begin with the Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of the lower rank matrix. BMA is more challenging than NMF as it imposes bounds on the product WH rather than on each of the low rank factors W and H. For very large input matrices, we extend our BMA algorithm to Block BMA that can scale to a large number of processors. In applications, such as HD video, where the input matrix to be factored is extremely large, distributed computation is inevitable and the network communication becomes a major performance bottleneck. Towards this end, we propose a novel distributed Communication Avoiding NMF (CANMF) algorithm that communicates only the right low rank factor to its neighboring machine. Finally, a general distributed HPC- NMF framework that uses HPC techniques in communication intensive NMF operations and suitable for broader class of NMF algorithms.
Graph-based algorithms and models for security, healthcare, and finance

(Georgia Institute of Technology, 2016-04-15) Tamersoy, Acar

Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking in people's computers with 99.61% true positive rate at 0.01% false positive rate; our application of ADAGE on malware detection (patent-pending) enables to detect malware in a streaming setting; and EDOCS (patent-pending) flags comment spammers among 197 thousand users on a social media platform accurately and preemptively. (2) Graph-induced Behavior Characterization: We derive new insights and knowledge that characterize certain behavior from graphs using statistical and algorithmic techniques. We present two examples: a study on identifying attributes of smoking and drinking abstinence and relapse from an addiction cessation social media community; and an exploratory analysis of how company insiders trade. Our work has already made impact to society: deployed by Symantec, AESOP is protecting over 120 million people worldwide from malware; EDOCS has been deployed by Yahoo and it guards multiple online communities from comment spammers.