search

SciML / OrdinaryDiffEq.jl

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
https://diffeq.sciml.ai/latest/
Other
522 stars 198 forks source link

Hamiltonian Boundary Value Methods (Energy Preserving Discrete Line Integral Methods) #1003

Open ChrisRackauckas opened 4 years ago

ChrisRackauckas commented 4 years ago

http://www.jnaiam.org/uploads/files/Volume5_Issues_1-2_Part_I/2.pdf

Recently, a new family of integrators (Hamiltonian Boundary Value Methods) has been introduced, which is able to precisely conserve the energy function of polynomial Hamiltonian systems and to provide a practical conservation of the energy in the non-polynomial case

Seems very useful for some physical simulations.

ChrisRackauckas commented 4 years ago

https://www.sciencedirect.com/science/article/abs/pii/S0096300319306265?via%3Dihub

ChrisRackauckas commented 4 years ago

https://link.springer.com/article/10.1007%2Fs11075-014-9825-0 https://arxiv.org/abs/1304.0974