We propose a "decomposition method" to prove non-asymptotic bound for the convergence of empirical measures in various dual norms. The main point is to show that if one measures convergence in duality with sufficiently regular observables, the convergence is much faster than for, say, merely Lipschitz observables. Actually, assuming $s$ derivatives with $s < d/2$ ($d$ the dimension) ensures an optimal rate of convergence of $1/\sqrt{n}$ ($n$ the number of samples). The method is flexible enough to apply to Markov chains which satisfy a geometric contraction hypothesis, assuming neither stationarity nor reversibility, with the same convergence speed up to a power of logarithm factor. Our results are stated as controls of the expected distance between the empirical measure and its limit, but we explain briefly how the classical method of bounded difference can be used to deduce concentration estimates.
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore（数学科学系，物理与数学科学学院，南洋理工大学，新加坡）；Faculty of Mathematics, Bielefeld University, Germany（数学学院，比勒菲尔德大学，德国）
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
ETH Zurich（苏黎世联邦理工学院）
近年来，深度人工神经网络（DNN）已经成功地用于大量计算问题的数值模拟，包括例如对象和面部识别、自然语言处理、欺诈检测、计算广告以及偏微分方程（PDE）的数值逼近。这些数值模拟表明，DNN似乎具有基本的灵活性，以克服维数灾难的意义上说，用于描述DNN的实参数的数量最多多项式增长在规定的近似精度$ \varepsilon > 0 $的倒数和DNN的目的是近似在这样的计算问题的功能的尺寸$ d \in \mathbb{N}$。科学文献中也有大量关于人工神经网络的严格数学近似结果，但只有少数特殊情况下，文献中的结果可以严格证明DNN在高维函数近似中的成功。本文的主要贡献是揭示了DNN确实克服了具有常数扩散和非线性漂移系数的Kolmogorov偏微分方程的数值逼近中的维数灾难。我们证明了用于描述所采用的DNN的参数的数量在PDE维度$ d \in \mathbb{N}$和规定的近似精度$ \varepsilon > 0 $的倒数中最多多项式地增长。我们证明中的一个关键因素是，用于近似PDE解的人工神经网络确实是一个具有大量隐藏层的深度人工神经网络。
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
University of Vienna（维也纳大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；ETH Zurich（苏黎世联邦理工学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Münster（明斯特大学）
Space-time deep neural network approximations for high-dimensional partial differential equations
Faculty of Mathematics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院数学学院）；Seminar for Applied Mathematics, Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系应用数学研讨班）；Department for Applied Mathematics, University of Freiburg（弗莱堡大学应用数学系）
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
Department of Mathematics & Informatics, University of Wuppertal（乌尔姆大学数学与信息学系）；School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳）数据科学学院及深圳大数据研究院）；Applied Mathematics: Institute for Analysis and Numerics, University of Münster（穆恩斯特大学应用数学系及分析与数值分析研究所）；Department of Mathematical Sciences, University of Arkansas（阿肯色大学数学科学系）；Center for Astrophysics, Space Physics, and Engineering Research, Baylor University（贝勒大学天体物理学、空间物理学与工程研究中心）
