26 papers · ranked by Valyu relevance
Jaroslaw Krzywanski, Marcin Sosnowski, Karolina Grabowska, Anna Zylka + 3 more
'Anna Zylka' 'Lukasz Lasek' 'Agnieszka Kijo-Kleczkowska' 'Bongju Kim'] This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends…
Jeannette M. Wing
Computational thinking will influence everyone in every field of endeavour. This vision poses a new educational challenge for our society, especially for our children. In thinking about computing, we need to be attuned to the three drivers of our field: science, technology and society. Accelerating technological…
Desmond J. Higham
I give a brief, non-technical, historical perspective on numerical analysis and optimization. I also touch on emerging trends and future challenges. This content is based on the short presentation that I made at the opening ceremony of The International Conference on Numerical Analysis and Optimization, which was held…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Brett K. Beaulieu-Jones, Casey S. Greene
Reproducing experiments is vital to science. Being able to replicate, validate and extend previous work also speeds new research projects. Reproducing computational biology experiments, which are scripted, should be straightforward. But reproducing such work remains challenging and time consuming. In the ideal world we…
Tibor Šimko, Lukas Alexander Heinrich, Clemens Lange, Adelina Eleonora Lintuluoto + 5 more
'Adelina Eleonora Lintuluoto' 'Danika Marina MacDonell' 'Audrius Mečionis' 'Diego Rodríguez Rodríguez' 'Parth Shandilya' 'Marco Vidal García'] We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing…
Stéphane Doncieux, Jean Liénard, Benoît Girard, Mohamed Hamdaoui + 1 more
'Joël Chaskalovic'] Computational models are of increasing complexity and their behavior may in particular emerge from the interaction of different parts. Studying such models becomes then more and more difficult and there is a need for methods and tools supporting this process. Multi-objective evolutionary algorithms…
Authors not listed
We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all with a unified and intuitive handling concept based on command…
Judith B. Rommel
Reliable predictions of the behaviour of chemical systems are essential across many industries, from nanoscale engineering over validation of advanced materials to nanotoxicity assessment in health and medicine. For the future we therefore envision a paradigm shift for the design of chemical simulations across all…
Ronald F. Boisvert, Michael J. Donahue, Daniel W. Lozier, Robert McMichael + 1 more
'Robert McMichael' 'Bert W. Rust'] In this paper we describe the role that mathematics plays in measurement science at NIST. We first survey the history behind NIST’s current work in this area, starting with the NBS Math Tables project of the 1930s. We then provide examples of more recent efforts in the application of…
Andrew Stokely, Lane Votapka, Marcus Hock, Abigail Teitgen + 3 more
We present the Netsci program - an open-source scientific software package that leverages GPU acceleration and a k-nearest-neighbor algorithm in order to estimate the mutual information (MI) between data in a set. The GPU acceleration presented here, as an improvement upon existing estimators, enables calculation…
Konrad Hinsen
Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication…
В. В. Аристов, А. В. Строганов
The well-known Turing machine is an example of a theoretical digital computer, and it was the logical basis of constructing real electronic computers. In the present paper we propose an alternative, namely, by formalising arithmetic operations in the ordinary computing device, we attempt to go to the analytical…
Sam P. de Visser
In the past few decades, computational resources have become more powerful every year and in addition methodology development has led to much more efficient techniques through parallelization of the calculations and the advent of density functional theory. These reasons make it possible for computational quantum…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg + 5 more
The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server…
Erickson Fajiculay, Chao-Ping Hsu
Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable…
Nathan R. Kern, Soohyung Park, Yiwei Cao, Wonpil Im
As high-performance computing provides the ability to generate and analyze ever larger simulation trajectories, the challenges in learning, applying, and sharing the best analytical practices become more salient. Extracting reproducible scientific insights from simulation requires a thorough understanding of many…
Alex Belianinov, Rama Vasudevan, Evgheni Strelcov, Chad Steed + 9 more
The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope…
Gregory A. Babbitt, Ernest P. Fokoue, Joshua R. Evans, Kyle I. Diller + 1 more
The application of statistical methods to comparatively framed questions about protein dynamics can potentially enable investigations of biomolecular function beyond the current sequence and structure based methods in comparative genomics. However, addressing this problem requires proper statistical inferences obtained…
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng + 4 more
It remains fundamentally unclear how to reprogram complex evolving systems. Here, we introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information.…
Authors not listed
Traditional and non-classical machine learning models for solid-state structure prediction have predominantly relied on compositional features (derived from properties of constituent elements) to predict the existence of structure and its properties. However, the lack of structural information can be a source of…
Huda Ibeid, Siping Meng, Oliver Dobon, Luke N. Olson + 1 more
—To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages. In this paper, we propose and validate a hybrid approach for performance modeling and prediction, which combines analytical and…
Ben Lambert, David J. Gavaghan, Simon Tavener
Biological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their…
Andrew McCluskey, Miguel Rivera, Antonia Mey
The role of computing in the chemical sciences is changing. Previously the domain of the theoretical or computational chemist, advanced digital skills, including data analysis and simulation, are becoming extremely relevant to all. Here, we discuss the importance of integrating computing and digital skills into an…
Sven Köppel, Bernd Ulmann, Lars Heimann, Dirk Killat
Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of traditional digital processors. General statements are made about the solution of…