Kaihua Ding, Ph.D.

Research interest: deep learning, machine learning, adjoint method, parallel computing, and numerical optimization.

Selected Publications

Kaihua Ding, Jingsong Cui, Mohammad Soltani and Jing Jin. Iterative Causal Segmentation PMSA Journal 2025 (accepted)
Kaihua Ding, Jingsong Cui, Mohammad Soltani and Jing Jin. Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy. PMSA Annual Meeting, General-3 2024.
Kaihua Ding and Krzysztof J. Fidkowski. Acceleration of Adjoint-Based Adaptation through Sub-Iterations for Unsteady Simulations. AIAA Paper, 2021-0155, 2021.
Kaihua Ding and Krzysztof J. Fidkowski. Acceleration of adjoint-based adaptation through sub-iterations. Journal of Computer & Fluids, Volume 202, 104491, 2020.
Kaihua Ding. Efficient output-based adaptation mechanics for high-order computational fluid dynamics methods. Ph.D. Dissertation, University of Michigan-Ann Arbor, 2018
Kaihua Ding and Krzysztof J. Fidkowski. Output error control using r-adaptation. AIAA Paper, 2017-4111, 2017.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Continuous adjoint based error estimation and r-refinement for the active flux method. AIAA Paper 2016-0832, 2016.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Acceleration techniques for adjoint-based error estimation and mesh adaptation. Eighth International Conference on Computational Fluid Dynamics, ICCFD8-0249, 2014.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Adjoint-based error estimation and mesh adaptation for the active flux method. AIAA Paper 2013-2942, 2013.