Justin Sirignano | DeepAI

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Featured Co-authors

Ziheng Wang
23 publications
Konstantinos Spiliopoulos
17 publications
Samuel N. Cohen
15 publications
Rama Cont
5 publications
Jonathan F. MacArt
4 publications
Deqing Jiang
2 publications
Jonathan B. Freund
2 publications
Jonathan MacArt
1 publication

∙ 05/10/2023

Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations

Numerically solving high-dimensional partial differential equations (PDE...

0 Deqing Jiang, et al. ∙

∙ 03/04/2023

Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS

Deep learning (DL) has recently emerged as a candidate for closure model...

0 Justin Sirignano, et al. ∙

∙ 08/06/2022

Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies

A deep learning (DL) closure model for large-eddy simulation (LES) is de...

9 Justin Sirignano, et al. ∙

∙ 07/10/2022

A Forward Propagation Algorithm for Online Optimization of Nonlinear Stochastic Differential Equations

Optimizing over the stationary distribution of stochastic differential e...

0 Ziheng Wang, et al. ∙

∙ 03/31/2022

Neural Q-learning for solving elliptic PDEs

Solving high-dimensional partial differential equations (PDEs) is a majo...

0 Samuel N. Cohen, et al. ∙

∙ 02/14/2022

Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations

We develop a new continuous-time stochastic gradient descent method for ...

0 Ziheng Wang, et al. ∙

∙ 08/19/2021

Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learning

Actor-critic algorithms are widely used in reinforcement learning, but a...

0 Ziheng Wang, et al. ∙

∙ 05/18/2021

PDE-constrained Models with Neural Network Terms: Optimization and Global Convergence

Recent research has used deep learning to develop partial differential e...

0 Justin Sirignano, et al. ∙

∙ 05/03/2021

Embedded training of neural-network sub-grid-scale turbulence models

The weights of a deep neural network model are optimized in conjunction ...

0 Jonathan F. MacArt, et al. ∙

∙ 11/20/2019

DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)

Machine learning for scientific applications faces the challenge of limi...

0 Jonathan B. Freund, et al. ∙

∙ 11/13/2019

Asymptotics of Reinforcement Learning with Neural Networks

We prove that a single-layer neural network trained with the Q-learning ...

0 Justin Sirignano, et al. ∙

∙ 07/09/2019

Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum

We analyze single-layer neural networks with the Xavier initialization i...

0 Justin Sirignano, et al. ∙

∙ 03/11/2019

Mean Field Analysis of Deep Neural Networks

We analyze multi-layer neural networks in the asymptotic regime of simul...

0 Justin Sirignano, et al. ∙

∙ 08/28/2018

Mean Field Analysis of Neural Networks: A Central Limit Theorem

Machine learning has revolutionized fields such as image, text, and spee...

4 Justin Sirignano, et al. ∙

∙ 03/19/2018

Universal features of price formation in financial markets: perspectives from Deep Learning

Using a large-scale Deep Learning approach applied to a high-frequency d...

0 Justin Sirignano, et al. ∙

∙ 10/11/2017

Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem

Stochastic gradient descent in continuous time (SGDCT) provides a comput...

0 Justin Sirignano, et al. ∙

∙ 08/24/2017

DGM: A deep learning algorithm for solving partial differential equations

High-dimensional PDEs have been a longstanding computational challenge. ...

0 Justin Sirignano, et al. ∙

∙ 11/17/2016

Stochastic Gradient Descent in Continuous Time

Stochastic gradient descent in continuous time (SGDCT) provides a comput...

0 Justin Sirignano, et al. ∙