Zixiang Ying

About

Zixiang Ying is a researcher at the forefront of bio-inspired robotics and computational fluid dynamics, specializing in the optimization of underwater propulsion systems. His work centers on developing jellyfish-inspired robots, specifically the mantle undulated propulsion robot (MUPRo), and creating advanced computational frameworks to predict and enhance their hydrodynamic performance. Ying’s major contributions include the integration of nonintrusive reduced-order models (NIROM) with proper orthogonal decomposition (POD) to reliably optimize robot propulsion, as demonstrated in his most-cited 2022 paper (8 citations). He has further advanced the field by pioneering deep learning-based reduced-order fluid-structure interaction models and a novel multiple proper orthogonal decomposition (MPOD) algorithm combined with long short-term memory (LSTM) neural networks to efficiently predict hydrodynamic forces. With a growing citation record and a focus on merging machine learning with fluid dynamics, Ying’s work is paving the way for more efficient, autonomous underwater vehicles inspired by nature. His innovative approach to parameter optimization and real-time force prediction marks him as a rising talent in bio-robotics and computational engineering.

Research Focus

Key Achievements

Top Papers

1. 1
   Propulsion optimization of a jellyfish-inspired robot based on a nonintrusive reduced-order model with proper orthogonal decomposition

   8 citations · 2022
2. 2
   Parameter optimization of the bio-inspired robot propulsion through the deep learning based reduced order fluid-structure interaction model

   7 citations · 2022
3. 3
   A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

   2 citations · 2023

Key Collaborators

Contact & Links