Randomized Residual-Based Error Estimators for Parametrized Equations by Patera, Anthony T.

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Stable Rank-Adaptive Dynamically Orthogonal Runge–Kutta Schemes by Charous, Aaron, Lermusiaux, Pierre F. J.

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A robust computational framework for simulating the dynamics of large assemblies of highly-flexible fibers immersed in viscous flow by Koshakji, Anwar, Chomette, Grégoire, Turner, Jeffrey, Jablonski, Jonathan, Haynes, Aisha, Carlucci, Donald, Giovanardi, Bianca, Radovitzky, Raúl A.

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Deep reinforcement learning for adaptive mesh refinement by Foucart, Corbin, Charous, Aaron, Lermusiaux, Pierre F.J.

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18.404J / 6.840J Theory of Computation, Fall 2006 by Sipser, Michael

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Sparse Multi-Reference Alignment: Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem by Ghosh, Subhroshekhar, Rigollet, Philippe

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Semidefinite Approximations of the Matrix Logarithm by Fawzi, Hamza, Saunderson, James, Parrilo, Pablo A.

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Approximation of Parametric Derivatives by the Empirical Interpolation Method by Grepl, Martin A., Patera, Anthony T., Ronquist, Einar M., Eftang, Jens L.

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Mathematics of the Neural Response by Bouvrie, Jacob Vincent, Rosasco, Lorenzo Andrea, Poggio, Tomaso A., Smale, S., Caponnetto, A.

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On the Distribution of Atkin and Elkies Primes by Shparlinski, Igor E., Sutherland II, Andrew Victor

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Commutation error in reduced order modeling of fluid flows by Koc, Birgul, Mohebujjaman, Muhammad, Mou, Changhong, Iliescu, Traian

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A transport-based multifidelity preconditioner for Markov chain Monte Carlo by Peherstorfer, Benjamin, Marzouk, Youssef M

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Scattering in Flatland: Efficient Representations via Wave Atoms by Demanet, Laurent, Ying, Lexing

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Compressive wave computation by Demanet, Laurent, Peyre, Gabriel

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Random Triangle Theory with Geometry and Applications by Edelman, Alan, Strang, Gilbert

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Generalization and Properties of the Neural Response by Bouvrie, Jake, Poggio, Tomaso, Rosasco, Lorenzo, Smale, Steve, Wibisono, Andre

“…Our work is based on the framework of hierarchical architectures introduced by Smale et al. in the paper "Mathematics of the Neural Response", Foundations of Computational Mathematics, 2010. We propose a generalized definition of the neural response and derived kernel that allows us to integrate some of the existing hierarchical algorithms in practice into our framework. …”
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Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation by Gómez, Pablo, Bungartz, Hans-Joachim, Peherstorfer, Benjamin

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Polynomial decomposition algorithms in signal processing by Su, Guolong, Ph. D. Massachusetts Institute of Technology

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Recent advances in computational methods for the power flow equations by Mehta, Dhagash, Molzahn, Daniel K., Turitsyn, Konstantin

“…While we do not claim to have exhausted the existing literature on all related methods, this tutorial paper introduces some of the recent advances in power flow solution methods to the wider power systems community as well as bringing attention from the computational mathematics and optimization communities to power systems problems. …”
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On structural decompositions of finite frames by Stokols, Logan, Theobold, Allison, Chan, Alice Z.-Y., Narayan, Sivaram K., Copenhaver, Martin Steven

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