Convergence rate for Galerkin approximation of the stochastic Allen-Cahn equations on 2D torus. - Ting Ma; Rongchan Zhu (CoRR, 2019)

Stochastic Heat Equations with Values in a Manifold via Dirichlet Forms. - Michael Röckner; Bo Wu; Rongchan Zhu; Xiangchan Zhu (SIAM J. Math. Anal., 2020)

Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs. - Peiyan Hu; Qi Meng; Bingguang Chen; Shiqi Gong; Yue Wang 017; Wei Chen 034; Rongchan Zhu; Zhi-Ming Ma; Tie-Yan Liu (CoRR, 2022)

Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations. - Rui Zhang 052; Peiyan Hu; Qi Meng; Yue Wang 017; Rongchan Zhu; Bingguang Chen; Zhi-Ming Ma; Tie-Yan Liu (CoRR, 2022)

Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations. - Shiqi Gong; Peiyan Hu; Qi Meng; Yue Wang 017; Rongchan Zhu; Bingguang Chen; Zhiming Ma; Hao Ni; Tie-Yan Liu (AAAI, 2023)

Monte Carlo Neural Operator for Learning PDEs via Probabilistic Representation. - Rui Zhang 052; Qi Meng; Rongchan Zhu; Yue Wang 017; Wenlei Shi; Shihua Zhang; Zhi-Ming Ma; Tie-Yan Liu (CoRR, 2023)

Riemannian Neural Geodesic Interpolant. - Jiawen Wu; Bingguang Chen; Yuyi Zhou; Qi Meng; Rongchan Zhu; Zhi-Ming Ma (CoRR, 2025)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation. - Rui Zhang 052; Qi Meng; Rongchan Zhu; Yue Wang 017; Wenlei Shi; Shihua Zhang; Zhi-Ming Ma; Tie-Yan Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2025)