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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/
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New tableaux for rodas4p, now rodas4p2 #1329

Open gstein3m opened 3 years ago

gstein3m commented 3 years ago

Would it be possible to add a new tableaux for a rodas4p2 method, which is an improvement of rodas4p, see https://link.springer.com/chapter/10.1007%2F978-3-030-53905-4_6

-- Rodas4P2

gamma = convert(T,1//4) a21= convert(T,3.000000000000000)
a31= convert(T,0.906377755268814) a32=-convert(T,0.189707390391685) a41= convert(T,3.758617027739064)
a42= convert(T,1.161741776019525) a43=-convert(T,0.849258085312803) a51= convert(T,7.089566927282776) a52= convert(T,4.573591406461604) a53=-convert(T,8.423496976860259) a54=-convert(T,0.959280113459775)

C21= convert(T,-12.00000000000000) C31= convert(T,-6.354581592719008)
C32= convert(T, 0.338972550544623) C41= convert(T,-8.575016317114033)
C42= convert(T,-7.606483992117508) C43= convert(T,12.224997650124820) C51= convert(T,-5.888975457523102) C52= convert(T,-8.157396617841821) C53= convert(T,24.805546872612922) C54= convert(T,12.790401512796979) C61= convert(T,-4.408651676063871) C62= convert(T,-6.692003137674639) C63= convert(T,24.625568527593117) C64= convert(T,16.627521966636085) C65= convert(T,-5.714285714285718)

c2=convert(T2,0.750000000000000) c3=convert(T2,0.321448134013046) c4=convert(T2,0.519745732277726) d1=convert(T, 0.250000000000000)
d2=convert(T,-0.500000000000000)
d3=convert(T,-0.189532918363016)
d4=convert(T, 0.085612108792769)

h21= convert(T, -5.323528268423303) h22= convert(T,-10.042123754867493) h23= convert(T, 17.175254928256965) h24=-convert(T, -5.079931171878093) h25= convert(T, -0.016185991706112) h31= convert(T, 6.984505741529879) h32=-convert(T, 6.914061169603662) h33= convert(T, -0.849178943070653) h34= convert(T, 18.104410789349338) h35=-convert(T, -3.516963011559032)

ChrisRackauckas commented 3 years ago

Yes! Just add a new RodasTableau:

https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/src/tableaus/rosenbrock_tableaus.jl#L296-L349

and all of the perform_step! stuff will work. For developer documentation on the ODE solver, see:

http://devdocs.sciml.ai/latest/contributing/adding_algorithms/

dynamic-queries commented 2 years ago

@ChrisRackauckas The tableau seems to be already in the package. https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/src/tableaus/rosenbrock_tableaus.jl#L351-L403

ChrisRackauckas commented 2 years ago

Needs docs