Articles with public access mandates - Justin SirignanoLearn more
Not available anywhere: 2
Adjoint-Trained Deep-Learning Closures of the Navier–Stokes Equations for 2D Nonequilibrium Flows
AS Nair, D Waidmann, J Sirignano, N Singh, M Panesi, JF MacArt
AIAA SCITECH 2024 Forum, 2860, 2024
Mandates: US Department of Defense
Adjoint Optimization of the BGK Equation with an Embedded Neural Network for Reduced-Order Modeling of Hypersonic Flows
N Daultry Ball, M Panesi, JF MacArt, J Sirignano
AIAA SCITECH 2024 Forum, 2859, 2024
Mandates: US Department of Energy, US Department of Defense, UK Engineering and …
Available somewhere: 21
DGM: A deep learning algorithm for solving partial differential equations
J Sirignano, K Spiliopoulos
Journal of computational physics 375, 1339-1364, 2018
Mandates: US National Science Foundation
Deep learning for mortgage risk
A Sadhwani, K Giesecke, J Sirignano
Journal of Financial Econometrics 19 (2), 313-368, 2021
Mandates: US National Science Foundation
Mean field analysis of neural networks: A central limit theorem
J Sirignano, K Spiliopoulos
Stochastic Processes and their Applications 130 (3), 1820-1852, 2020
Mandates: US National Science Foundation
Mean field analysis of neural networks: A law of large numbers
J Sirignano, K Spiliopoulos
SIAM Journal on Applied Mathematics 80 (2), 725-752, 2020
Mandates: US National Science Foundation
DPM: A deep learning PDE augmentation method with application to large-eddy simulation
J Sirignano, JF MacArt, JB Freund
Journal of Computational Physics 423, 109811, 2020
Mandates: US National Science Foundation, US Department of Energy
Mean field analysis of deep neural networks
J Sirignano, K Spiliopoulos
arXiv preprint arXiv:1903.04440, 2019
Mandates: US National Science Foundation
Stochastic gradient descent in continuous time
J Sirignano, K Spiliopoulos
SIAM Journal on Financial Mathematics 8 (1), 933-961, 2017
Mandates: US National Science Foundation
Embedded training of neural-network subgrid-scale turbulence models
JF MacArt, J Sirignano, JB Freund
Physical Review Fluids 6 (5), 050502, 2021
Mandates: US National Science Foundation, US Department of Energy
Risk analysis for large pools of loans
J Sirignano, K Giesecke
Management Science 65 (1), 107-121, 2019
Mandates: US National Science Foundation
Stochastic gradient descent in continuous time: A central limit theorem
J Sirignano, K Spiliopoulos
Stochastic Systems 10 (2), 124-151, 2020
Mandates: US National Science Foundation
Inference for large financial systems
K Giesecke, G Schwenkler, JA Sirignano
Mathematical Finance 30 (1), 3-46, 2020
Mandates: US National Science Foundation
PDE-constrained models with neural network terms: Optimization and global convergence
J Sirignano, J MacArt, K Spiliopoulos
Journal of Computational Physics 481, 112016, 2023
Mandates: US National Science Foundation
Deep learning closure models for large-eddy simulation of flows around bluff bodies
J Sirignano, JF MacArt
Journal of Fluid Mechanics 966, A26, 2023
Mandates: US National Science Foundation, US Department of Energy
Asymptotics of reinforcement learning with neural networks
J Sirignano, K Spiliopoulos
Stochastic Systems 12 (1), 2-29, 2022
Mandates: US National Science Foundation
Deep learning closure of the Navier–Stokes equations for transition-continuum flows
AS Nair, J Sirignano, M Panesi, JF MacArt
AIAA journal 61 (12), 5484-5497, 2023
Mandates: US Department of Defense
Continuous‐time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations
Z Wang, J Sirignano
Mathematical Finance 34 (2), 348-424, 2024
Mandates: UK Engineering and Physical Sciences Research Council
Online adjoint methods for optimization of PDEs
J Sirignano, K Spiliopoulos
Applied Mathematics & Optimization 85 (2), 18, 2022
Mandates: US National Science Foundation
Neural Q-learning for solving PDEs
SN Cohen, D Jiang, J Sirignano
Journal of Machine Learning Research 24 (236), 1-49, 2023
Mandates: UK Engineering and Physical Sciences Research Council, UK Research & Innovation
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