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Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Readme

DiffEqOperators.jl is a package for finite difference discretization of partial differential equations. It serves two purposes:

- Building fast lazy operators for high order non-uniform finite differences.
- Automated finite difference discretization of symbolically-defined PDEs.

For the operators, both centered and upwind operators are provided, for domains of any dimension, arbitrarily spaced grids, and for any order of accuracy. The cases of 1, 2, and 3 dimensions with an evenly spaced grid are optimized with a convolution routine from

NNlib.jl. Care is taken to give efficiency by avoiding unnecessary allocations, using purpose-built stencil compilers, allowing GPUs and parallelism, etc. Any operator can be concretized as an

Array, a

BandedMatrixor a sparse matrix.

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation which contains the unreleased features.

using OrdinaryDiffEq, ModelingToolkit, DiffEqOperators## Parameters, variables, and derivatives

@parameters t x @variables u(..) @derivatives Dt'

~~t @derivatives Dxx''~~x## 1D PDE and boundary conditions

eq = Dt(u(t,x)) ~ Dxx(u(t,x)) bcs = [u(0,x) ~ cos(x), u(t,0) ~ exp(-t), u(t,Float64(pi)) ~ -exp(-t)]

## Space and time domains

domains = [t ∈ IntervalDomain(0.0,1.0), x ∈ IntervalDomain(0.0,Float64(pi))]

## PDE system

pdesys = PDESystem(eq,bcs,domains,[t,x],[u])

## Method of lines discretization

dx = 0.1 order = 2 discretization = MOLFiniteDifference(dx,order)

## Convert the PDE problem into an ODE problem

prob = discretize(pdesys,discretization)

## Solve ODE problem

sol = solve(prob,Tsit5(),saveat=0.1)

using DiffEqOperators, OrdinaryDiffEqnknots = 100 h = 1.0/(nknots+1) knots = range(h, step=h, length=nknots) ord_deriv = 2 ord_approx = 2

const Δ = CenteredDifference(ord_deriv, ord_approx, h, nknots) const bc = Dirichlet0BC(Float64)

t0 = 0.0 t1 = 0.03 u0 = u_analytic.(knots, t0)

step(u,p,t) = Δ

bcu prob = ODEProblem(step, u0, (t0, t1)) alg = KenCarp4() sol = solve(prob, alg)