[FGN13] Xiaobing Feng, Roland Glowinski, and Michael Neilan. Recent developments in numerical methods for fully nonlinear second order partial differential equations. siam REVIEW, 55(2):205-267, 2013.
[GW79] Ian Gladwell and R Wait. A survey of numerical methods for partial differential equations. Oxford: Clarendon Press, 1979.
[HF08] Isom Herron and Michael Foster. Partial differential equations in fluid dynamics. Cambridge Univ. Press, 2008.
[HJE18] Jiequn Han, Arnulf Jentzen, and Weinan E. Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences, 115(34):8505-8510, 2018.
[HKRC18] Ya-Ping Hsieh, Ali Kavis, Paul Rolland, and Volkan Cevher. Mirrored Langevin dynamics. Advances in Neural Information Processing Systems, 31, 2018.
[HMW21] Lu-Jing Huang, Mateusz B Majka, and Jian Wang. Approximation of heavy-tailed distributions via stable-driven sdes. 2021.
[Ise09] Arieh Iserles. A first course in the numerical analysis of differential equations. Number 44. Cambridge university press, 2009.
[KKL+21] George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422-440, 2021.
[KLY21] Yuehaw Khoo, Jianfeng Lu, and Lexing Ying. Solving parametric pde problems with artificial neural networks. European Journal of Applied Mathematics, 32(3):421-435, 2021.
[KPRS22] Gitta Kutyniok, Philipp Petersen, Mones Raslan, and Reinhold Schneider. A theoretical analysis of deep neural networks and parametric pdes. Constructive Approximation, 55(1):73-125, 2022.
[KRP12] S Kazem, JA Rad, and K Parand. Radial basis functions methods for solving fokker-planck equation. Engineering Analysis with Boundary Elements, 36(2):181-189, 2012.
[Lam21] Andrew Lamperski. Projected stochastic gradient Langevin algorithms for constrained sampling and non-convex learning. In Conference on Learning Theory, pages 2891–2937. PMLR, 2021.
[LAT04] Fawang Liu, Vo Anh, and Ian Turner. Numerical solution of the space fractional fokker-planck equation. Journal of Computational and Applied Mathematics, 166(1):209-219, 2004.
$\left[\mathrm{LCL}^{+}21\right]$  Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, and Jose Blanchet. Machine learning for elliptic pdes: fast rate generalization bound, neural scaling law and minimax optimality. arXiv preprint arXiv:2110.06897, 2021.
[LL22] Jianfeng Lu and Yulong Lu. A priori generalization error analysis of two-layer neural networks for solving high dimensional schrödinger eigenvalue problems. Communications of the American Mathematical Society, 2(01):1-21, 2022.
[LLMD18] Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong. Pde-net: Learning pdes from data. In International conference on machine learning, pages 3208-3216. PMLR, 2018.
[LLW21] Jianfeng Lu, Yulong Lu, and Min Wang. A priori generalization analysis of the deep ritz method for solving high dimensional elliptic equations. arXiv preprint arXiv:2101.01708, 2021.
[LT00] Irena Lasiecka and Roberto Triggiani. Control theory for partial differential equations: Volume 1, Abstract parabolic systems: Continuous and approximation theories, volume 1. Cambridge University Press, 2000.
[MCF15] Yi-An Ma, Tianqi Chen, and Emily Fox. A complete recipe for stochastic gradient mcmc. Advances in neural information processing systems, 28, 2015.
[MFBR20] Ulysse Marteau-Ferey, Francis Bach, and Alessandro Rudi. Non-parametric models for nonnegative functions. Advances in neural information processing systems, 33:12816-12826, 2020.
[MFBR22] Ulysse Marteau-Ferey, Francis Bach, and Alessandro Rudi. Sampling from arbitrary functions via psd models. In International Conference on Artificial Intelligence and Statistics, pages 2823-2861. PMLR, 2022.
[MP18] Carlo Morosi and Livio Pizzocchero. On the constants for some fractional gagliardo-nirenberg and sobolev inequalities. Expositiones Mathematicae, 36(1):32-77, 2018.