(Include but not limited to)
Track 1: Computational Mechanics Theory and Numerical Methods
Fundamentals of Computational Mechanics and Numerical Methods
Finite Element Method and Extended Finite Element Method
Finite Difference Method and Finite Volume Method
Boundary Element Method and Meshless Method
Spectral Method and High-Precision Numerical Formulation
Multiscale Mechanical Modeling and Cross-Scale Computation
Multifield Coupling Numerical Simulation
Numerical Solution of Nonlinear Mechanical Problems
Numerical Simulation of Contact and Collision Problems
Numerical Methods for Fracture and Damage Mechanics
Structural Optimization and Topology Optimization Algorithms
Molecular Dynamics and Multiscale Simulation
Particle Materials and Discrete Element Method
Computational Fluid Dynamics and Fluid-Structure Coupling
Uncertainty Quantification in Computational Mechanics
Performance Evaluation and Benchmarking in Computational Mechanics
Development of Computational Mechanics Software and Tools
Applications and Cases in Computational Mechanics
Track 2: Scientific Machine Learning and Data-Driven Modeling
Theoretical and Methodological Foundations of Scientific Machine Learning
Physical Information Neural Network (PINN)
Neural Operators and Deep Operator Networks
Data-Driven Constitutive Models and Reduced-Order Modeling
Machine Learning Accelerated Numerical Solvers
Applications of Deep Learning in Computational Mechanics
Applications of Reinforcement Learning in Structural Optimization
Applications of Graph Neural Networks in Mechanical Modeling
Generative AI and Mechanical Data Enhancement
Transfer Learning and Multi-Holistic Modeling
Uncertainty Quantification and Bayesian Methods
Interpretable AI and Integration with Mechanical Models
Causal Inference in Scientific Machine Learning
Integration of Scientific Machine Learning with High-Performance Computing
......