Mean Field Analysis of Deep Neural Networks
Sirignano, J
Spiliopoulos, K
(11 Mar 2019)
doi:10.48550/arxiv.1903.04440
Mean Field Analysis of Neural Networks: A Law of Large Numbers
Sirignano, J
Spiliopoulos, K
(02 May 2018)
doi:10.48550/arxiv.1805.01053
Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
Sirignano, J
Spiliopoulos, K
(09 Jul 2019)
doi:10.48550/arxiv.1907.04108
Asymptotics of Reinforcement Learning with Neural Networks
Sirignano, J
Spiliopoulos, K
(13 Nov 2019)
doi:10.48550/arxiv.1911.07304
DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)
Freund, J
MacArt, J
Sirignano, J
(20 Nov 2019)
doi:10.48550/arxiv.1911.09145
Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem
Sirignano, J
Spiliopoulos, K
Stochastic Systems
volume 10
issue 2
124-151
(Jun 2020)
doi:10.1287/stsy.2019.0050
Online Adjoint Methods for Optimization of PDEs
Sirignano, J
Spiliopoulos, K
(23 Jan 2021)
doi:10.48550/arxiv.2101.09621
Embedded training of neural-network sub-grid-scale turbulence models
MacArt, J
Sirignano, J
Freund, J
(03 May 2021)
doi:10.48550/arxiv.2105.01030
PDE-constrained Models with Neural Network Terms: Optimization and Global Convergence
Sirignano, J
MacArt, J
Spiliopoulos, K
(18 May 2021)
doi:10.48550/arxiv.2105.08633
Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies
Sirignano, J
MacArt, J
(06 Aug 2022)
doi:10.48550/arxiv.2208.03498