Pre-computed Datasets Research Articles

Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver

Computational Fluid Dynamics (CFD) traditionally relies on long-standing numerical simulation strategies for the Navier–Stokes equations. Recently, interest in data-driven hybrid CFD solvers has spiked, leveraging pre-computed datasets to enhance various weak links inside existing solvers, such as closure models, under-resolved physics, or even to guide numerical resolution strategies. Running these hybrid solvers, notably in High Performance Computing (HPC) environments, presents specific challenges. In particular, context-aware deep learning (e.g. Convolutional (CNN) and Graph (GNN) Neural Networks) is promising for this task, but requires passing data representations between the physics solver and the neural network. In relevant industrial configurations, CFD meshes can be Cartesian but highly irregular, or unstructured, both of which do not match the pixel/voxel structure needed to run CNNs. In addition, discrepancies in programming language and libraries are common between CFD and machine learning applications. This work explores the many challenges of running a parallel hybrid solver in an HPC context, through the coupling of the AVBP CFD solver with neural networks in turbulent combustion and wall friction modeling applications. The knowledge gained is showcased in this article, as well as assembled in an actionable open-source library.

Morphing-Enabled Path Planning for Flying Tensegrity Robots as a Semidefinite Program.

The development of deformable drones is of high importance but presents significant challenges. Such drones can be based on tensegrity structures, which leaves open the questions of configuration-space path planning for such robots. In this paper we propose a method that takes advantage of a simplified encoding of the drone’s shape, allowing to turn the path planning into a sequence of semidefinite programs. The mapping from the simplified description and the actual tensegrity configuration is done via a data-driven method, using a pre-computed dataset of statically stable configurations and their outer Löwner-John ellipsoids, as well as eigendecompositions of the ellipsoid matrices. Together it allows rapid containment check, whose computational cost depends linearly on the number of dataset entries. Thus, the proposed method offloads computationally-intensive parts to the offline dataset generation procedure, speeding up the algorithm execution.

Development of a 6-DOF Dynamic Velocity Prediction Program for offshore racing yachts

Thanks to high lift-to-drag ratios, hydrofoils are of great interest for high-speed vessels. Modern sailing yachts fitted with foils have thus reached impressively high speeds on the water. But this hydrodynamic efficiency is achieved at the expense of stability. Accurate tradeoffs are therefore needed to ensure both performance and safety. While usual Velocity Prediction Programs (VPPs) are inadequate to assess dynamic stability, the varying nature of the offshore racing environment further complicates the task.Dynamic simulation in the time-domain is thus necessary to help architects assess their designs. This paper presents a system-based numerical tool which aims at predicting the dynamic behavior of offshore sailing yachts. A 6 degrees of freedom (DOF) algorithm is used, calculating loads as a superposition of several components (hull, appendage, sails). Part of them are computed at runtime while the others use pre-computed dataset, allowing a good compromise between efficiency and flexibility.Three 6DOF simulations of an existing offshore trimaran (a maneuver, unsteady wind conditions and quartering seas) are presented. They underline the interest of dynamic studies, demonstrating how important the yacht state history is to the understanding of her instantaneous behavior and showing that dynamic simulations open a different field of optimization than VPPs.

A Low-Complexity 6DOF Magnetic Tracking System Based on Pre-Computed Data Sets for Wearable Applications

We present a 6DOF magnetic tracking system based on a low-complexity algorithm, operating with an Inertial Measurement Unit (IMU) orientation estimation and regression functions formed with simulated data sets, capable of running using only a single microcontroller unit (MCU), for use in low-complexity wearable and wireless systems. A prototype based on a commercial magnetometer and IMU, a Cortex-M4 MCU was implemented and tested in both static and dynamic conditions, using a VICON motion tracking system as reference. Static and dynamic spatial accuracy performance is 2.6mm and 5.4mm respectively, after applying a calibration procedure based on a two layers Neural Network (NN) and a measured data set. Comparison with the state-of-the-art, supported by a defined Figure-of-Merit (FoM) show excellent performance compared to commercial and research systems in a low-complexity and portable solution.

Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

Natural hazards such as tropical cyclones is a topic of wider interest and operational forecast of their landfall, maximum sustained winds, storm surge height and associated extent of inland inundation is a challenging topic having wider socio-economic implication. Recent advancements in computational power and development of sophisticated models have resulted in better understanding the dynamics of atmosphere and ocean. Forecast quality have improved to a great extent however there are still constraints in computation time and cost for real-time operations. Soft computing techniques in the broad domain of computational intelligence are widely recognized today having diverse practical applications and beneficial value across multiple disciplines. The present study is an effort on application of soft computing techniques such as Artificial Neural Networks, Genetic Algorithm, and Genetic Programming to predict storm surge and inundation characteristics resulting from tropical cyclones. The coast of Odisha adjoining the Bay of Bengal is considered for this case study. Historically, the Odisha coast is known to be highly vulnerable to strike from maximum number of high intense tropical cyclones that form over Bay of Bengal region. Recently in a separate study the authors have developed a comprehensive pre-computed dataset on storm surges and inundation scenarios from historical cyclones that made landfall over coastal Odisha State. Present study is an effort to effectively utilize the pre-computed dataset for real-time operation using soft computing techniques and that performs rapid computation to aid emergency preparedness and planning operations. Study performed several numerical experiments using various soft computing techniques and best possible configuration for real-time operation is developed. An inter-comparison exercise was also carried out to skill assess the performance of various soft computing techniques. The authors believe that this study has immense potential for real-time operations and that can be extended to other coastal regions in India.

PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes

BackgroundBacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer.ResultsWe introduce PanACEA (Pan-genome Atlas with Chromosome Explorer and Analyzer), which utilizes locally-computed interactive web-pages to view ordered pan-genome data. It consists of multi-tiered, hierarchical display pages that extend from pan-chromosomes to both core and variable regions to single genes. Regions and genes are functionally annotated to allow for rapid searching and visual identification of regions of interest with the option that user-supplied genomic phylogenies and metadata can be incorporated. PanACEA’s memory and time requirements are within the capacities of standard laptops. The capability of PanACEA as a research tool is demonstrated by highlighting a variable region important in differentiating strains of Enterobacter hormaechei.ConclusionsPanACEA can rapidly translate the results of pan-chromosome programs into an intuitive and interactive visual representation. It will empower researchers to visually explore and identify regions of the pan-chromosome that are most biologically interesting, and to obtain publication quality images of these regions.

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants

The number of sequenced genomes of representatives within the green lineage is rapidly increasing. Consequently, comparative sequence analysis has significantly altered our view on the complexity of genome organization, gene function, and regulatory pathways. To explore all this genome information, a centralized infrastructure is required where all data generated by different sequencing initiatives is integrated and combined with advanced methods for data mining. Here, we describe PLAZA, an online platform for plant comparative genomics (http://bioinformatics.psb.ugent.be/plaza/). This resource integrates structural and functional annotation of published plant genomes together with a large set of interactive tools to study gene function and gene and genome evolution. Precomputed data sets cover homologous gene families, multiple sequence alignments, phylogenetic trees, intraspecies whole-genome dot plots, and genomic colinearity between species. Through the integration of high confidence Gene Ontology annotations and tree-based orthology between related species, thousands of genes lacking any functional description are functionally annotated. Advanced query systems, as well as multiple interactive visualization tools, are available through a user-friendly and intuitive Web interface. In addition, detailed documentation and tutorials introduce the different tools, while the workbench provides an efficient means to analyze user-defined gene sets through PLAZA's interface. In conclusion, PLAZA provides a comprehensible and up-to-date research environment to aid researchers in the exploration of genome information within the green plant lineage.

EDGAR: A software framework for the comparative analysis of prokaryotic genomes

BackgroundThe introduction of next generation sequencing approaches has caused a rapid increase in the number of completely sequenced genomes. As one result of this development, it is now feasible to analyze large groups of related genomes in a comparative approach. A main task in comparative genomics is the identification of orthologous genes in different genomes and the classification of genes as core genes or singletons.ResultsTo support these studies EDGAR – "Efficient Database framework for comparative Genome Analyses using BLAST score Ratios" – was developed. EDGAR is designed to automatically perform genome comparisons in a high throughput approach. Comparative analyses for 582 genomes across 75 genus groups taken from the NCBI genomes database were conducted with the software and the results were integrated into an underlying database. To demonstrate a specific application case, we analyzed ten genomes of the bacterial genus Xanthomonas, for which phylogenetic studies were awkward due to divergent taxonomic systems. The resultant phylogeny EDGAR provided was consistent with outcomes from traditional approaches performed recently and moreover, it was possible to root each strain with unprecedented accuracy.ConclusionEDGAR provides novel analysis features and significantly simplifies the comparative analysis of related genomes. The software supports a quick survey of evolutionary relationships and simplifies the process of obtaining new biological insights into the differential gene content of kindred genomes. Visualization features, like synteny plots or Venn diagrams, are offered to the scientific community through a web-based and therefore platform independent user interface , where the precomputed data sets can be browsed.

Parallel Poisson disk sampling

Sampling is important for a variety of graphics applications include rendering, imaging, and geometry processing. However, producing sample sets with desired efficiency and blue noise statistics has been a major challenge, as existing methods are either sequential with limited speed, or are parallel but only through pre-computed datasets and thus fall short in producing samples with blue noise statistics. We present a Poisson disk sampling algorithm that runs in parallel and produces all samples on the fly with desired blue noise properties. Our main idea is to subdivide the sample domain into grid cells and we draw samples concurrently from multiple cells that are sufficiently far apart so that their samples cannot conflict one another. We present a parallel implementation of our algorithm running on a GPU with constant cost per sample and constant number of computation passes for a target number of samples. Our algorithm also works in arbitrary dimension, and allows adaptive sampling from a user-specified importance field. Furthermore, our algorithm is simple and easy to implement, and runs faster than existing techniques.