Rate My Professor Zhongqiang Zhang

Among his key publications is the book Numerical methods for stochastic partial differential equations with white noise, co-authored with G.E. Karniadakis and published by Springer in 2017. Other notable works include Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators with L. Lu, P. Jin, G. Pang, and G.E. Karniadakis (Nature Machine Intelligence, 2021); VPINNs: Variational Physics-Informed Neural Networks For Solving Partial Differential Equations with E. Kharazmi and G.E.M. Karniadakis (arXiv preprint); hp-VPINNs: variational physics-informed neural networks with domain decomposition with E. Kharazmi and G.E.M. Karniadakis (Computer Methods in Applied Mechanics and Engineering, 2021); A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data with L. Lu et al. (Computer Methods in Applied Mechanics and Engineering, 2022); and Optimal strong convergence of finite element methods for one-dimensional stochastic elliptic equations with fractional noise with W. Cao and Z. Hao (Journal of Scientific Computing, 2022). In 2017, as Assistant Professor, Zhang secured $113,932 in funding from the Department of Defense's Multidisciplinary University Research Initiative via a sub-award from Brown University to conduct mathematical analysis and simulations of fractional advection-reaction-diffusion equations pertinent to combustion, functional microfluidics, materials, and systems biology. His research has achieved significant impact, with over 8,800 citations on Google Scholar.