Module scientific_computing_kernels 

Scientific Computing Kernel Methods

This module implements kernel methods for scientific computing applications, including physics-informed kernels, differential equation kernels, conservation law kernels, and multiscale methods.

References

• Raissi et al. (2019): “Physics-informed neural networks”
• Karniadakis et al. (2021): “Physics-informed machine learning”
• Chen & Sideris (2020): “Finite element method with interpolation at the nodes”
• E & Yu (2018): “The Deep Ritz Method”

Structs

MultiscaleKernel

Multiscale Kernel for hierarchical phenomena

PhysicsInformedConfig

Configuration for physics-informed kernels

PhysicsInformedKernel

Physics-Informed Kernel

Enums

PhysicalSystem

Types of physical systems