[1] Q. Chang and D. Li, “Well-posedness and dynamics of wave equations with nonlinear damping and moving boundary,” Discrete and Continuous Dynamical Systems-B, pp. 0–0, 2024. 
[2] Q. Chang and D. Li, “Well-posedness and dynamics of 2d navier–stokes equations with moving boundary,” Journal of Mathematical Physics, vol. 64, no. 2, p. 022 702, 2023. 
[3] Q. Chang, D. Li, C. Sun, and S. V. Zelik, “Deterministic and random attractors for a wave equation with sign changing damping,” Izvestiya. Mathematics, vol. 87, no. 1, pp. 161–210, 2023. 
[4] Q. Chang and D. Li, “Continuity of dynamical behaviors for strongly damped wave equations with perturbation,” Journal of Mathematical Physics, vol. 63, no. 5, p. 052 702, 2022. 
[5] D. Li, Q. Chang, and C. Sun, “Pullback attractors for a critical degenerate wave equation with 
time-dependent damping,” Nonlinear Analysis: Real World Applications, vol. 63, p. 103 421, 2022. 
[6] Q. Chang, D. Li, and C. Sun, “Random attractors for stochastic time-dependent damped wave equation with critical exponents,” Discrete Continuous Dynamical Systems - B, vol. 22, no. 11, 2020.7 
[7] Q. Chang, D. Li, and C. Sun, “Dynamics for a stochastic degenerate parabolic equation,” Computers mathematics with applications, vol. 77, no. 9, pp. 2407–2431, 2019. 
[8] J. Wang, Q. Chang, Q. Chang, Y. Liu, and N. R. Pal, “Weight noise injection-based mlps with group lasso penalty: Asymptotic convergence and application to node pruning,” IEEE Transactions on Cybernetics, pp. 1–19, 2018. 
[9] D. Li, C. Sun, and Q. Chang, “Global attractor for degenerate damped hyperbolic equations,” Journal of Mathematical Analysis Applications, vol. 453, no. 1, pp. 1–19, 2017. 
[10] J. Wang, Q. Cai, Q. Chang, and J. M. Zurada, “Convergence analyses on sparse feedforward neural networks via group lasso regularization,” Information Sciences, vol. 381, pp. 250–269, 2017.