Machine Learning Applications in Numerical Computing

Integration of machine learning techniques with numerical methods including neural networks for PDE solving, reinforcement learning for optimization, and data-driven numerical methods.

• Apply neural networks to approximate solutions of differential equations
• Use machine learning for automatic parameter tuning in numerical algorithms
• Implement physics-informed neural networks (PINNs) for PDE solutions
• Apply reinforcement learning to numerical optimization problems
• Use data-driven approaches for model reduction and surrogate modeling
• Integrate machine learning with traditional numerical methods