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The DPG Method for the Convection-Reaction Problem Revisited

Demkowicz, Leszek D.; Roberts, Nathan V.

We study both conforming and non-conforming versions of the practical DPG method for the convection-reaction problem. We determine that the most common approach for DPG stability analysis (construction of a local Fortin operator) is infeasible for the convection-reaction problem. We then develop a line of argument based on the direct construction of a global Fortin operator; we find that employing a polynomial enrichment for the test space does not suffice for this purpose, motivating the introduction of a (two-element) subgrid mesh. The argument combines mathematical analysis with numerical experiments

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TYPE Other Report YEAR 2021
OSTIDOI

Large-Scale Trajectory Analysis via Feature Vectors

Rintoul, Mark D.; Jones, Jessica L.; Newton, Benjamin D.; Wisniewski, Kyra L.; Wilson, Andrew T.; Ginaldi, Melissa J.; Waddell, Cleveland A.; Goss, Kenneth G.; Ward, Katrina J.

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

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TYPE Other Report YEAR 2021
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The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Environmental Modelling and Software

Razavi, Saman; Jakeman, Anthony; Saltelli, Andrea; Prieur, Clémentine; Iooss, Bertrand; Borgonovo, Emanuele; Plischke, Elmar; Lo Piano, Samuele; Iwanaga, Takuya; Becker, William; Tarantola, Stefano; Guillaume, Joseph H.A.; Jakeman, John D.; Gupta, Hoshin; Melillo, Nicola; Rabitti, Giovanni; Chabridon, Vincent; Duan, Qingyun; Sun, Xifu; Smith, Stefán; Sheikholeslami, Razi; Hosseini, Nasim; Asadzadeh, Masoud; Puy, Arnald; Kucherenko, Sergei; Maier, Holger R.

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

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TYPE Journal Article YEAR 2021
ScopusOSTIDOI
Results 851–875 of 9,998
Results 851–875 of 9,998