Functional SDE approximation inspired by a deep operator network architecture

5 February 2024

Papers citing "Functional SDE approximation inspired by a deep operator network architecture"

11 / 11 papers shown

Title
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures Charles Miranda Janina Enrica Schutte David Sommer Martin Eigel 48 3 0 06 Nov 2023
Diffusion Models: A Comprehensive Survey of Methods and Applications Ling Yang Zhilong Zhang Yingxia Shao Shenda Hong Runsheng Xu Yue Zhao Wentao Zhang Tengjiao Wang Ming-Hsuan Yang DiffM MedIm 307 1,374 0 02 Sep 2022
Physics-Informed Deep Neural Operator Networks S. Goswami Aniruddha Bora Yue Yu George Karniadakis PINN AI4CE 65 104 0 08 Jul 2022
Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations C. Marcati Christoph Schwab 58 39 0 15 Dec 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 451 2,384 0 18 Oct 2020
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence Lénaïc Chizat Pierre Roussillon Flavien Léger François-Xavier Vialard Gabriel Peyré OT 48 114 0 15 Jun 2020
Solving the Kolmogorov PDE by means of deep learning C. Beck S. Becker Philipp Grohs Nor Jaafari Arnulf Jentzen 46 95 0 01 Jun 2018
Optimal approximation of piecewise smooth functions using deep ReLU neural networks P. Petersen Felix Voigtländer 191 475 0 15 Sep 2017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations Weinan E Jiequn Han Arnulf Jentzen 115 797 0 15 Jun 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.3K 149,842 0 22 Dec 2014
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 175 4,251 0 04 Jun 2013