Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2023)

Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. - Dingcheng Luo; Lianghao Cao; Peng Chen 024; Omar Ghattas; J. Tinsley Oden (J. Comput. Phys., 2023)

Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs. - Dingcheng Luo; Joshua Chen; Peng Chen 024; Omar Ghattas (CoRR, 2024)

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC. - Joseph Kirchhoff; Dingcheng Luo; Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2024)

SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. - Dingcheng Luo; Peng Chen 024; Thomas O'Leary-Roseberry; Umberto Villa; Omar Ghattas (J. Open Source Softw., 2024)

A Cascaded Vernier Sensing Structure Combined With Ensemble Learning for High-Sensitivity and Large-Range Bovine Serum Albumin Detection. - Yong Zhao 005; Chaofan Li; Li-Ming Zhou; Fucheng Xiang; Dingcheng Luo; Rui-Jie Tong; Mao-Qing Chen (IEEE Trans. Instrum. Meas., 2025)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2025)