Justin A. Sirignano
Orcid: 0000-0002-0971-1349Affiliations:
- University of Oxford, Mathematical Institute, UK
According to our database1,
Justin A. Sirignano authored at least 34 papers
between 2012 and 2026.
Collaborative distances:
Collaborative distances:
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Bibliography
2026
Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows.
CoRR, May, 2026
Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit.
CoRR, May, 2026
CoRR, March, 2026
CoRR, January, 2026
Global Convergence of Deep Galerkin and PINN Methods for Solving Partial Differential Equations.
SIAM J. Financial Math., 2026
OGF: An online gradient flow method for optimizing the statistical steady-state time averages of unsteady turbulent flows.
J. Comput. Phys., 2026
Online optimisation of machine learning collision models to accelerate direct molecular simulation of rarefied gas flows.
J. Comput. Phys., 2026
2025
oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation.
CoRR, October, 2025
Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions.
CoRR, July, 2025
Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies.
CoRR, July, 2025
Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks.
CoRR, January, 2025
2024
2023
PDE-constrained models with neural network terms: Optimization and global convergence.
J. Comput. Phys., May, 2023
CoRR, 2023
Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations.
CoRR, 2023
CoRR, 2023
2022
CoRR, 2022
A Forward Propagation Algorithm for Online Optimization of Nonlinear Stochastic Differential Equations.
CoRR, 2022
Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations.
CoRR, 2022
2021
Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learning.
CoRR, 2021
2020
SIAM J. Appl. Math., 2020
DPM: A deep learning PDE augmentation method with application to large-eddy simulation.
J. Comput. Phys., 2020
2019
Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum.
CoRR, 2019
2018
J. Comput. Phys., 2018
2017
2016
2012
A Forward-backward Algorithm for Stochastic Control Problems - Using the Stochastic Maximum Principle as an Alternative to Dynamic Programming.
Proceedings of the ICORES 2012, 2012