Description
Book SynopsisRonald A. DeVore and Angela Kunoth, Prologue to Multiscale, Nonlinear and Adaptive Approximation II.- Ronald A. DeVore and Angela Kunoth, Introduction: Wolfgang Dahmen's mathematical work (as of 2009).- Markus Bachmayr and Albert Cohen, Multilevel Representations of Random Fields and Sparse Approximations of Solutions to Random PDEs.- Hassan Ballout and Yvon Maday and Christophe Prud'homme, Nonlinear compressive reduced basis approximation for multi-parameter elliptic problem.- Ido Ben Shaul and Shai Dekel, Sparse Besov Space Analysis of Representations in Machine Learning.- Benjamin Berkels and Peter Binev, Joint Denoising and Line Distortion Correction for Raster-Scanned Image Series.- Dietrich Braess and Wolfgang Hackbusch, The Approximation of Cauchy-Stieltjes and Laplace-Stieltjes Functions.- Andrea Bonito and Diane Guignard, Approximating Partial Differential Equations without Boundary Conditions.- Albert Cohen and Ronald DeVore and Eitan Tadmor, Constructions of Bounded Solutions of div u= f in Critical Spaces.- Jan-Christopher Cohrs and Benjamin Berkels, On the importance of the e-regularization of the distribution-dependent MumfordShah model for hyperspectral image segmentation.- Ronald DeVore, Guergana Petrova and Przemyslaw Wojtaszczyk, A Note on Best n-term Approximation for Generalized Wiener Classes.- Lars Grasedyck, Sebastian Krämer and Dieter Moser, Stable Truncation and Root-Independent Normalization of Tree Tensor Networks.- Diane Guignard and Olga Mula, Tree-Based Nonlinear Reduced Modeling.- Helmut Harbrecht and Michael Multerer, Samplets: Wavelet Concepts for Scattered Data.- Michael Herty, Adrian Kolb, and Siegfried Müller, A novel multilevel approach for the efficient computation of random hyperbolic conservation laws.- Kamen G. Ivanov, Gerard Kerkyacharian, George Kyriazis, and Pencho Petrushev, On the Construction of Bases and Frames with Applications.- Angela Kunoth and Mathias Oster and Reinhold Schneider, Towards a Continuous Mathematical Model for the Analysis of Classes of Deep Neural Networks.- Dominique Picard, Unstoppable Mathematicians.- Reinhold Schneider and Mathias Oster, Some Thoughts on Compositional Tensor Networks.- Rob Stevenson, Efficient least squares discretizations for Unique Continuation and Cauchy problems.
