About
I build data-efficient machine-learning methods — neural operators, multi-fidelity surrogates, and reinforcement-learning controllers — for civil-engineering systems where simulation is expensive and observations are sparse.
I am a Ph.D. candidate in Civil Engineering at the University of Notre Dame, working with Prof. Patrick Brewick in the Brewick Group. My research sits at the intersection of structural dynamics, scientific machine learning, and structural health monitoring.
I earned my M.S. in Civil Engineering from Tianjin University in 2022, including a research visit to the LIFT Laboratory at Nanyang Technological University. Before Notre Dame, I was a research assistant at the Institute of Urban Smart Transportation and Safety Maintenance at Shenzhen University.
Research Priorities
My work sits at the intersection of structural dynamics, scientific machine learning, and structural health monitoring. Three priorities guide current projects:
Scientific ML for Structural Dynamics
Neural operators and multi-fidelity surrogates for full-field response under uncertainty.
Reinforcement-Learning Control
Adaptive controllers for vibration mitigation and real-time hybrid simulation.
Wireless Sensing & SHM
MEMS sensors, generative denoising, and field deployment for infrastructure monitoring.
Selected Publications
- Jichuan Tang, R. G. McClarren, C. Sweet, P. T. Brewick. “A Full-field Extended Deep Operator Network as a Spatio-temporal Surrogate for Structural Dynamics.” Engineering Structures, 2025. Under Review
- N. Li, Jichuan Tang, Z.-X. Li, S. Gao. “Reinforcement Learning Control Method for Real-time Hybrid Simulation based on Deep Deterministic Policy Gradient Algorithm.” Structural Control and Health Monitoring, 2022. (Co-first author)
