XD2 Lab
Extreme-Condition Dynamics & Design Lab — designing systems that operate near their instability limits
Sicheng He
Assistant Professor
Mechanical and Aerospace Engineering
University of Tennessee, Knoxville
University of Tennessee, Knoxville
sicheng@utk.edu
Dougherty Engineering Bldg
Knoxville, TN 37996
Welcome
Our mission: To develop structured and differentiable representations of complex dynamical systems that enable scalable analysis, physical insight, and optimal design.
The XD2 Lab develops mathematical and computational frameworks to represent, interpret, and optimize complex nonlinear dynamical systems — with emphasis on engineering systems that operate near their instability limits.
Latest News
Jul 2026
Rohit Kanchi to present at the prestigious NASA Ames Applied Modeling & Simulation Seminar on July 2.
Jun 2026
Paper “SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity” accepted at IROS 2026 — led by Yulun Zhuang and Yanran Ding (UMich).
Jun 2026
Rohit Kanchi wins 2026 AIAA Aviation MDO Best Student Paper Runner-Up ($1,000) — paper.
Jun 2026
Congrats to Ben Melanson on accepting a position at Naval Surface Warfare Center (NSWC) — important next step in his career!
Dec 2025
Rohit Kanchi and Ben Melanson presenting at NeurIPS 2025!
Jul 2025
Seed grant from UTK AI Tennessee Initiative for AI research led by Vladimir Sobes ($50K total, $25K share).
Dec 2024
New lab website launched!
Aug 2023
Prof. He joins UTK MABE as Assistant Professor
Open Positions
Ph.D. positions available!
We are looking for motivated students interested in MDO, computational physics, scientific computing, and fluid mechanics.
Graduate students from MABE, EECS, ISE, and applied math at UTK are welcome to reach out.
View DetailsResearch Areas
Structured Representations
- Time-spectral methods
- Torus methods for quasi-periodic dynamics
- Floquet stability theory
Operator-Theoretic Analysis
- Resolvent analysis
- Differentiable modal decompositions
- Optimization-compatible reduced coordinates
Optimization, Control & Learning
- Adjoint-based stability optimization
- Multidisciplinary design optimization
- Scientific ML for inference and design