Research Interests研究方向
Scientific Machine Learning科学机器学习
Neural operator learning for PDEs, data-driven nonlocal constitutive models, deep learning methods for differential equations, theory of physics-guided learning.面向偏微分方程的神经算子学习、数据驱动的非局部本构模型、微分方程深度学习方法,以及物理引导学习理论。
Numerical Methods for PDEs偏微分方程数值方法
High-order numerical schemes, stability and convergence analysis, absorbing boundary conditions, efficient algorithms for partial differential equations on unbounded domains.高阶数值格式、稳定性与收敛性分析、吸收边界条件,以及无界区域偏微分方程的高效算法。
Nonlocal Models非局部模型
Peridynamic models and neural operators, nonlocal wave equations, fractional differential equations, material modeling with nonlocal interactions.近场动力学模型与神经算子、非局部波动方程、分数阶微分方程,以及具有非局部相互作用的材料建模。