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Abdul-Wahid B.,University of Notre Dame | Abdul-Wahid B.,The Interdisciplinary Center | Yu L.,University of Notre Dame | Rajan D.,University of Notre Dame | And 7 more authors.
2012 IEEE 8th International Conference on E-Science, e-Science 2012

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour. ©2012 IEEE. Source

Wang K.,Stanford University | Gretarsson J.T.,Stanford University | Main A.,Stanford University | Farhat C.,Institute for Computational and Mathematical Engineering
20th AIAA Computational Fluid Dynamics Conference 2011

A robust, accurate, and computationally efficient interface tracking algorithm is a key component of an embedded/immersed computational framework for the solution of fluid-structure interaction problems with complex and deformable geometries. To a large extent, the design of such an algorithm has focused on the case of a closed embedded interface and a Cartesian Computational Fluid Dynamics (CFD) grid. Here, two robust and efficient interface tracking computational algorithms capable of operating on structured as well as unstructured three-dimensional CFD grids are presented. The first one is based on a projection approach, whereas the second one is based on a collision approach. The first algorithm is faster. However, it is restricted to closed interfaces and resolved enclosed volumes. The second algorithm is therefore slower. However, it can handle open shell surfaces and underresolved enclosed volumes. Both computational algorithms exploit the bounding box hierarchy technique and its parallel distributed implementation to efficiently store and retrieve the elements of the discretized embedded interface. They are illustrated, and their respective performances are assessed and contrasted, with the solution of three-dimensional, nonlinear, dynamic fluid-structure interaction problems pertaining to aeroelastic and underwater implosion applications. © 2011 by Kevin Wang. Source

Shechtman Y.,Stanford University | Weiss L.E.,Stanford University | Backer A.S.,Institute for Computational and Mathematical Engineering | Sahl S.J.,Stanford University | And 2 more authors.
Nano Letters

We employ a novel framework for information-optimal microscopy to design a family of point spread functions (PSFs), the Tetrapod PSFs, which enable high-precision localization of nanoscale emitters in three dimensions over customizable axial (z) ranges of up to 20 μm with a high numerical aperture objective lens. To illustrate, we perform flow profiling in a microfluidic channel and show scan-free tracking of single quantum-dot-labeled phospholipid molecules on the surface of living, thick mammalian cells. (Figure Presented). © 2015 American Chemical Society. Source

Marsden A.L.,Institute for Computational and Mathematical Engineering | Feinstein J.A.,Stanford University
Current Opinion in Pediatrics

Purpose of review Recent methodological advances in computational simulations are enabling increasingly realistic simulations of hemodynamics and physiology, driving increased clinical utility. We review recent developments in the use of computational simulations in pediatric and congenital heart disease, describe the clinical impact in modeling in single-ventricle patients, and provide an overview of emerging areas. Recent findings Multiscale modeling combining patient-specific hemodynamics with reduced order (i.e., mathematically and computationally simplified) circulatory models has become the de-facto standard for modeling local hemodynamics and 'global' circulatory physiology. We review recent advances that have enabled faster solutions, discuss new methods (e.g., fluid structure interaction and uncertainty quantification), which lend realism both computationally and clinically to results, highlight novel computationally derived surgical methods for single-ventricle patients, and discuss areas in which modeling has begun to exert its influence including Kawasaki disease, fetal circulation, tetralogy of Fallot (and pulmonary tree), and circulatory support. Summary Computational modeling is emerging as a crucial tool for clinical decision-making and evaluation of novel surgical methods and interventions in pediatric cardiology and beyond. Continued development of modeling methods, with an eye towards clinical needs, will enable clinical adoption in a wide range of pediatric and congenital heart diseases. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Source

Saibaba A.K.,Tufts University | Kitanidis P.K.,Yang and Yamazaki Environment and oEnergy Building | Kitanidis P.K.,Institute for Computational and Mathematical Engineering
Advances in Water Resources

We consider the computational challenges associated with uncertainty quantification involved in parameter estimation such as seismic slowness and hydraulic transmissivity fields. The reconstruction of these parameters can be mathematically described as inverse problems which we tackle using the geostatistical approach. The quantification of uncertainty in the geostatistical approach involves computing the posterior covariance matrix which is prohibitively expensive to fully compute and store. We consider an efficient representation of the posterior covariance matrix at the maximum a posteriori (MAP) point as the sum of the prior covariance matrix and a low-rank update that contains information from the dominant generalized eigenmodes of the data misfit part of the Hessian and the inverse covariance matrix. The rank of the low-rank update is typically independent of the dimension of the unknown parameter. The cost of our method scales as O(mlogm) where m dimension of unknown parameter vector space. Furthermore, we show how to efficiently compute measures of uncertainty that are based on scalar functions of the posterior covariance matrix. The performance of our algorithms is demonstrated by application to model problems in synthetic travel-time tomography and steady-state hydraulic tomography. We explore the accuracy of the posterior covariance on different experimental parameters and show that the cost of approximating the posterior covariance matrix depends on the problem size and is not sensitive to other experimental parameters. © 2015 Elsevier Ltd. Source

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