# An Adaptive WENO Collocation Method for Differential Equations with Random Coefficients

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Department of Mathematics, Michigan State University, East Lansing, MI 48824; USA

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Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA

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Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA

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Department of Mathematics, University of Houston, Houston, TX 49931, USA

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Author to whom correspondence should be addressed.

Academic Editors: Zhongqiang Zhang and Mohsen Zayernouri

Received: 1 February 2016 / Revised: 20 April 2016 / Accepted: 21 April 2016 / Published: 3 May 2016

(This article belongs to the Special Issue New Trends in Applications of Orthogonal Polynomials and Special Functions)

The stochastic collocation method for solving differential equations with random inputs has gained lots of popularity in many applications, since such a scheme exhibits exponential convergence with smooth solutions in the random space. However, in some circumstance the solutions do not fulfill the smoothness requirement; thus a direct application of the method will cause poor performance and slow convergence rate due to the well known Gibbs phenomenon. To address the issue, we propose an adaptive high-order multi-element stochastic collocation scheme by incorporating a WENO (Weighted Essentially non-oscillatory) interpolation procedure and an adaptive mesh refinement (AMR) strategy. The proposed multi-element stochastic collocation scheme requires only repetitive runs of an existing deterministic solver at each interpolation point, similar to the Monte Carlo method. Furthermore, the scheme takes advantage of robustness and the high-order nature of the WENO interpolation procedure, and efficacy and efficiency of the AMR strategy. When the proposed scheme is applied to stochastic problems with non-smooth solutions, the Gibbs phenomenon is mitigated by the WENO methodology in the random space, and the errors around discontinuities in the stochastic space are significantly reduced by the AMR strategy. The numerical experiments for some benchmark stochastic problems, such as the Kraichnan-Orszag problem and Burgers’ equation with random initial conditions, demonstrate the reliability, efficiency and efficacy of the proposed scheme.