The newest journal from the Society for Industrial and Applied Mathematics, SIAM/ASA Journal on Uncertainty Quantification (JUQ), launched recently with its first seven papers publishing online to Volume 1.
Offered jointly by SIAM and the American Statistical Association, the journal publishes research articles presenting significant mathematical, statistical, algorithmic, and application advances in uncertainty quantification and is dedicated to nurturing synergistic interactions between these and related areas.
Under the leadership of senior editor Max Gunzburger, editors-in-chief James Berger and Donald Estep, and more than 35 others comprising the editorial board, the journal will feature continuous electronic publication, with complimentary access in 2013.
If the first few research articles are any indication—covering the analysis and quantification of uncertainty in areas as far-reaching as finance, disaster preparedness, and porous media flows—the journal promises great depth and breadth of coverage in uncertainty quantification research.
Some of the papers you will read in JUQ‘s maiden volume include the following:
- “Mean Exit Times and the Multilevel Monte Carlo Method” by Desmond Higham, Xuerong Mao, Mikolaj Roj, Qingshuo Song, and George Yin
- “Variance Components and Generalized Sobol’ Indices” by Art Owen
- “Formulating Natural Hazard Policies Under Uncertainty” by Jerome and Seth Stein
- “A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations” by Victor Picheny and David Ginsbourger
- “Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Loève Expansion” by Bernard Haasdonk, Karsten Urban, and Bernhard Wieland
- “A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation” by K. Singh, A. Sandu, M. Jardak, K. W. Bowman, and M. Lee
- “A Posteriori Estimates for Backward SDEs” by Christian Bender and Jessica Steiner
Access the full text of these research articles on the SIAM website.
Authors are encouraged to submit their uncertainty quantification work for consideration on the JUQ website.
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