Author
J.M. Hyman
Bio: J.M. Hyman is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Propagation of uncertainty & Sensitivity analysis. The author has an hindex of 2, co-authored 2 publications receiving 1009 citations.
Papers
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TL;DR: This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification inredictive modeling.
Abstract: Predictive modeling's effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification
988 citations
01 May 2005
TL;DR: Patch dynamics bridges the gap between the time and space scales at which the microscopic models operate, helping predict system-level behavior.
Abstract: The engineering analysis and microscopic simulations required for predicting materials' properties from atomistic descriptions require approaches for predicting macroscopic properties. Patch dynamics bridges the gap between the time and space scales at which the microscopic models operate, helping predict system-level behavior.
26 citations
Cited by
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University of California, Berkeley1, Stellenbosch University2, University of Jyväskylä3, University of Cambridge4, Google5, University of Toronto6, University of Birmingham7, Temple University8, Amazon.com9, University of British Columbia10, University of Georgia11, University of Oxford12, Los Alamos National Laboratory13, University of California, Irvine14
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
Abstract: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
7,624 citations
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
4,786 citations
TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.
3,903 citations
TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Abstract: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence. le formats and multiple sequence alignments, dealing with 3D macromolecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
3,855 citations
TL;DR: The pymatgen library as mentioned in this paper is an open-source Python library for materials analysis that provides a well-tested set of structure and thermodynamic analyses relevant to many applications, and an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments.
2,364 citations