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
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
Deep Learning Closure of the Navier-Stokes Equations for Transition-Continuum Flows
 Nair, A Sirignano, J Panesi, M MacArt, J (21 Mar 2023) doi:10.48550/arxiv.2303.12114
Online Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows
 Ball, N MacArt, J Sirignano, J (20 Nov 2024) doi:10.48550/arxiv.2411.13423
Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
 Lam, S Sirignano, J Spiliopoulos, K (14 Jan 2025) doi:10.48550/arxiv.2501.08040
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