| DGM: A deep learning algorithm for solving partial differential equations J Sirignano, K Spiliopoulos Journal of computational physics 375, 1339-1364, 2018 | 3054 | 2018 |
| Universal features of price formation in financial markets: perspectives from deep learning J Sirignano, R Cont Machine learning and AI in finance, 5-15, 2021 | 512 | 2021 |
| Deep learning for mortgage risk A Sadhwani, K Giesecke, J Sirignano Journal of Financial Econometrics 19 (2), 313-368, 2021 | 346 | 2021 |
| 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 | 275 | 2020 |
| Mean field analysis of neural networks: A central limit theorem J Sirignano, K Spiliopoulos Stochastic Processes and their Applications 130 (3), 1820-1852, 2020 | 267 | 2020 |
| Deep learning for limit order books JA Sirignano Quantitative Finance 19 (4), 549-570, 2019 | 212 | 2019 |
| 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 | 189 | 2020 |
| Mean field analysis of deep neural networks J Sirignano, K Spiliopoulos arXiv preprint arXiv:1903.04440, 2019 | 115 | 2019 |
| Large portfolio asymptotics for loss from default K Giesecke, K Spiliopoulos, RB Sowers, JA Sirignano Mathematical Finance 25 (1), 77-114, 2015 | 89 | 2015 |
| Mean field analysis of neural networks J Sirignano, K Spiliopoulos arXiv preprint arXiv:1805.01053 1 (5), 2018 | 80 | 2018 |
| Embedded training of neural-network subgrid-scale turbulence models JF MacArt, J Sirignano, JB Freund Physical Review Fluids 6 (5), 050502, 2021 | 77 | 2021 |
| Stochastic gradient descent in continuous time J Sirignano, K Spiliopoulos SIAM Journal on Financial Mathematics 8 (1), 933-961, 2017 | 74 | 2017 |
| Risk analysis for large pools of loans J Sirignano, K Giesecke Management Science 65 (1), 107-121, 2019 | 66 | 2019 |
| Fluctuation analysis for the loss from default K Spiliopoulos, JA Sirignano, K Giesecke Stochastic Processes and their Applications 124 (7), 2322-2362, 2014 | 46 | 2014 |
| Deep learning closure models for large-eddy simulation of flows around bluff bodies J Sirignano, JF MacArt Journal of Fluid Mechanics 966, A26, 2023 | 45 | 2023 |
| Stochastic gradient descent in continuous time: A central limit theorem J Sirignano, K Spiliopoulos Stochastic Systems 10 (2), 124-151, 2020 | 45 | 2020 |
| Inference for large financial systems K Giesecke, G Schwenkler, JA Sirignano Mathematical Finance 30 (1), 3-46, 2020 | 44 | 2020 |
| Large-scale loan portfolio selection JA Sirignano, G Tsoukalas, K Giesecke Operations Research 64 (6), 1239-1255, 2016 | 40 | 2016 |
| PDE-constrained models with neural network terms: Optimization and global convergence J Sirignano, J MacArt, K Spiliopoulos Journal of Computational Physics 481, 112016, 2023 | 37 | 2023 |
| 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 | 25 | 2023 |
