| Scalable computation of monge maps with general costs J Fan, S Liu, S Ma, Y Chen, H Zhou arXiv preprint arXiv:2106.03812, 4, 2021 | 35 | 2021 |
| Neural Parametric Fokker--Planck Equation S Liu, W Li, H Zha, H Zhou SIAM Journal on Numerical Analysis 60 (3), 1385-1449, 2022 | 26 | 2022 |
| Neural monge map estimation and its applications J Fan, S Liu, S Ma, H Zhou, Y Chen arXiv preprint arXiv:2106.03812, 2021 | 26 | 2021 |
| Learning stochastic behaviour from aggregate data S Ma, S Liu, H Zha, H Zhou International Conference on Machine Learning, 7258-7267, 2021 | 16 | 2021 |
| Wasserstein Hamiltonian flow with common noise on graph J Cui, S Liu, H Zhou SIAM Journal on Applied Mathematics 83 (2), 484-509, 2023 | 10 | 2023 |
| Optimal control for stochastic nonlinear Schrödinger equation on graph J Cui, S Liu, H Zhou SIAM Journal on Control and Optimization 61 (4), 2021-2042, 2023 | 9 | 2023 |
| What is a stochastic Hamiltonian process on finite graph? An optimal transport answer J Cui, S Liu, H Zhou Journal of Differential Equations 305, 428-457, 2021 | 9 | 2021 |
| Learning high dimensional wasserstein geodesics S Liu, S Ma, Y Chen, H Zha, H Zhou arXiv preprint arXiv:2102.02992, 2021 | 9 | 2021 |
| Parametric fokker-planck equation W Li, S Liu, H Zha, H Zhou Geometric Science of Information: 4th International Conference, GSI 2019 …, 2019 | 9 | 2019 |
| Stochastic wasserstein hamiltonian flows J Cui, S Liu, H Zhou Journal of Dynamics and Differential Equations, 1-37, 2023 | 8 | 2023 |
| Parameterized wasserstein hamiltonian flow H Wu, S Liu, X Ye, H Zhou SIAM Journal on Numerical Analysis 63 (1), 360-395, 2025 | 6 | 2025 |
| Parameterized wasserstein gradient flow Y Jin, S Liu, H Wu, X Ye, H Zhou Journal of Computational Physics 524, 113660, 2025 | 2 | 2025 |
| A first-order computational algorithm for reaction-diffusion type equations via primal-dual hybrid gradient method S Liu, S Liu, S Osher, W Li Journal of Computational Physics 500, 112753, 2024 | 2 | 2024 |
| Numerical Analysis on Neural Network Projected Schemes for Approximating One Dimensional Wasserstein Gradient Flows X Zuo, J Zhao, S Liu, S Osher, W Li arXiv preprint arXiv:2402.16821, 2024 | 2 | 2024 |
| A supervised learning scheme for computing hamilton-jacobi equation via density coupling J Cui, S Liu, H Zhou arXiv preprint arXiv:2401.15954, 2024 | 2 | 2024 |
| Approximating the optimal transport plan via particle-evolving method S Liu, H Sun, H Zha arXiv preprint arXiv:2105.06088, 2021 | 2 | 2021 |
| Numerical analysis of a first-order computational algorithm for reaction-diffusion equations via the primal-dual hybrid gradient method S Liu, X Zuo, S Osher, W Li arXiv preprint arXiv:2401.14602, 2024 | 1 | 2024 |
| A Particle-Evolving Method for Approximating the Optimal Transport Plan S Liu, H Sun, H Zha Geometric Science of Information: 5th International Conference, GSI 2021 …, 2021 | 1 | 2021 |
| A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations S Liu, S Osher, W Li arXiv preprint arXiv:2411.06278, 2024 | | 2024 |
| Numerical computation and analysis related to optimal transport theory S Liu Georgia Institute of Technology, 2022 | | 2022 |
Haomin ZhouSchool of MathematicsPreverjeni e-poštni naslov na math.gatech.edu
