(Topics include but are not limited to)
High dimensional numerical computation and approximation theory
Numerical and Intelligent Solutions for Partial Differential Equations
Differential Equations and Physical Information Neural Networks (PINN)
Scientific Machine Learning (SciML) and Symbolic Regression
Data driven modeling and system identification
Sparse recognition and dynamic discovery
Uncertainty quantification and surrogate models
Operator Learning and Neural Operators (DeepONet, FNO)
Graph Neural Networks and Complex Network Modeling
Interpretable machine learning and causal inference
Deep learning theory (generalization, convergence, robustness)
Reinforcement Learning and Optimal Control
Generative modeling and probabilistic modeling
Bayesian method and variational inference
Multi scale modeling and coupling of multiple physical fields
Big data analysis and dimensionality reduction techniques
Optimization algorithm and computational complexity
High performance computing and distributed machine learning
Anti problem and regularization methods
Industrial applications (fluid mechanics, materials science, biomedical, financial mathematics, etc.)