diffeqr is a package for solving differential equations in R. It utilizes DifferentialEquations.jl for its core routines to give high performance solving of ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), and differential-algebraic equations (DAEs) directly in R.

If you have any questions, or just want to chat about solvers/using the package, please feel free to chat in the Gitter channel. For bug reports, feature requests, etc., please submit an issue.

diffeqr is registered into CRAN. Thus to add the package, use:

`install.packages("diffeqr")`

To install the master branch of the package (for developers), use:

`::install_github('SciML/diffeqr', build_vignettes=T) devtools`

Note that the first invocation of
`diffeqr::diffeq_setup()`

will install both Julia and the
required packages if they are missing. If you wish to have it use an
existing Julia binary, make sure that `julia`

is found in the
path. For more information see the `julia_setup()`

function
from JuliaCall.

diffeqr provides a direct wrapper over DifferentialEquations.jl. The namespace is setup so that the standard syntax of Julia translates directly over to the R environment. There are two things to keep in mind:

- All DifferentialEquations.jl commands are prefaced by
`de$`

- All commands with a
`!`

are replaced with`_bang`

, for example`solve!`

becomes`solve_bang`

.

Let’s solve the linear ODE `u'=1.01u`

. First setup the
package:

`<- diffeqr::diffeq_setup() de `

Define the derivative function `f(u,p,t)`

.

```
<- function(u,p,t) {
f return(1.01*u)
}
```

Then we give it an initial condition and a time span to solve over:

```
<- 1/2
u0 <- c(0., 1.) tspan
```

With those pieces we define the `ODEProblem`

and
`solve`

the ODE:

```
= de$ODEProblem(f, u0, tspan)
prob = de$solve(prob) sol
```

This gives back a solution object for which `sol$t`

are
the time points and `sol$u`

are the values. We can treat the
solution as a continuous object in time via

`$.(0.2) sol`

and a high order interpolation will compute the value at
`t=0.2`

. We can check the solution by plotting it:

`plot(sol$t,sol$u,"l")`

Now let’s solve the Lorenz equations. In this case, our initial condition is a vector and our derivative functions takes in the vector to return a vector (note: arbitrary dimensional arrays are allowed). We would define this as:

```
<- function(u,p,t) {
f = p[1]*(u[2]-u[1])
du1 = u[1]*(p[2]-u[3]) - u[2]
du2 = u[1]*u[2] - p[3]*u[3]
du3 return(c(du1,du2,du3))
}
```

Here we utilized the parameter array `p`

. Thus we use
`diffeqr::ode.solve`

like before, but also pass in parameters
this time:

```
<- c(1.0,0.0,0.0)
u0 <- c(0.0,100.0)
tspan <- c(10.0,28.0,8/3)
p <- de$ODEProblem(f, u0, tspan, p)
prob <- de$solve(prob) sol
```

The returned solution is like before except now `sol$u`

is
an array of arrays, where `sol$u[i]`

is the full system at
time `sol$t[i]`

. It can be convenient to turn this into an R
matrix through `sapply`

:

`<- sapply(sol$u,identity) mat `

This has each row as a time series. `t(mat)`

makes each
column a time series. It is sometimes convenient to turn the output into
a `data.frame`

which is done via:

`<- as.data.frame(t(mat)) udf `

Now we can use `matplot`

to plot the timeseries
together:

`matplot(sol$t,udf,"l",col=1:3)`

Now we can use the Plotly package to draw a phase plot:

`::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines') plotly`

Plotly is much prettier!

If we want to have a more accurate solution, we can send
`abstol`

and `reltol`

. Defaults are
`1e-6`

and `1e-3`

respectively. Generally you can
think of the digits of accuracy as related to 1 plus the exponent of the
relative tolerance, so the default is two digits of accuracy. Absolute
tolernace is the accuracy near 0.

In addition, we may want to choose to save at more time points. We do
this by giving an array of values to save at as `saveat`

.
Together, this looks like:

```
<- 1e-8
abstol <- 1e-8
reltol <- 0:10000/100
saveat <- de$solve(prob,abstol=abstol,reltol=reltol,saveat=saveat)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines') plotly
```

We can also choose to use a different algorithm. The choice is done using a string that matches the Julia syntax. See the ODE tutorial for details. The list of choices for ODEs can be found at the ODE Solvers page. For example, let’s use a 9th order method due to Verner:

`<- de$solve(prob,de$Vern9(),abstol=abstol,reltol=reltol,saveat=saveat) sol `

Note that each algorithm choice will cause a JIT compilation.

One way to enhance the performance of your code is to define the
function in Julia so that way it is JIT compiled. diffeqr is built using
the JuliaCall
package, and so you can utilize the Julia JIT compiler. We expose
this automatically over ODE functions via `jitoptimize_ode`

,
like in the following example:

```
<- function(u,p,t) {
f = p[1]*(u[2]-u[1])
du1 = u[1]*(p[2]-u[3]) - u[2]
du2 = u[1]*u[2] - p[3]*u[3]
du3 return(c(du1,du2,du3))
}<- c(1.0,0.0,0.0)
u0 <- c(0.0,100.0)
tspan <- c(10.0,28.0,8/3)
p <- de$ODEProblem(f, u0, tspan, p)
prob <- diffeqr::jitoptimize_ode(de,prob)
fastprob <- de$solve(fastprob,de$Tsit5()) sol
```

Note that the first evaluation of the function will have an ~2 second lag since the compiler will run, and all subsequent runs will be orders of magnitude faster than the pure R function. This means it’s great for expensive functions (ex. large PDEs) or functions called repeatedly, like during optimization of parameters.

We can also use the JuliaCall functions to directly define the function in Julia to eliminate the R interpreter overhead and get full JIT compilation:

```
<- JuliaCall::julia_eval("
julf function julf(du,u,p,t)
du[1] = 10.0*(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2] - (8/3)*u[3]
end")
::julia_assign("u0", u0)
JuliaCall::julia_assign("p", p)
JuliaCall::julia_assign("tspan", tspan)
JuliaCall= JuliaCall::julia_eval("ODEProblem(julf, u0, tspan, p)")
prob3 = de$solve(prob3,de$Tsit5()) sol
```

To demonstrate the performance advantage, let’s time them all:

```
> system.time({ for (i in 1:100){ de$solve(prob ,de$Tsit5()) }})
user system elapsed6.69 0.06 6.78
> system.time({ for (i in 1:100){ de$solve(fastprob,de$Tsit5()) }})
user system elapsed0.11 0.03 0.14
> system.time({ for (i in 1:100){ de$solve(prob3 ,de$Tsit5()) }})
user system elapsed0.14 0.02 0.15
```

This is about a 50x improvement!

Using Julia’s ModelingToolkit for tracing to JIT compile via Julia has a few known limitations:

- It requires that all of the function calls are tracable. Scalar
functions like
`cos`

and`sin`

all fall into this category. Notably, matrix multiplication is not supported. - It will have a compilation lag on the first call.

Solving stochastic differential equations (SDEs) is the similar to
ODEs. To solve an SDE, you use `diffeqr::sde.solve`

and give
two functions: `f`

and `g`

, where
`du = f(u,t)dt + g(u,t)dW_t`

```
<- diffeqr::diffeq_setup()
de <- function(u,p,t) {
f return(1.01*u)
}<- function(u,p,t) {
g return(0.87*u)
}<- 1/2
u0 <- c(0.0,1.0)
tspan <- de$SDEProblem(f,g,u0,tspan)
prob <- de$solve(prob)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = sol$t, y = sol$u, type = 'scatter', mode = 'lines') plotly
```

Let’s add diagonal multiplicative noise to the Lorenz attractor. diffeqr defaults to diagonal noise when a system of equations is given. This is a unique noise term per system variable. Thus we generalize our previous functions as follows:

```
<- function(u,p,t) {
f = p[1]*(u[2]-u[1])
du1 = u[1]*(p[2]-u[3]) - u[2]
du2 = u[1]*u[2] - p[3]*u[3]
du3 return(c(du1,du2,du3))
}<- function(u,p,t) {
g return(c(0.3*u[1],0.3*u[2],0.3*u[3]))
}<- c(1.0,0.0,0.0)
u0 <- c(0.0,1.0)
tspan <- c(10.0,28.0,8/3)
p <- de$SDEProblem(f,g,u0,tspan,p)
prob <- de$solve(prob,saveat=0.005)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines') plotly
```

Using a JIT compiled function for the drift and diffusion functions can greatly enhance the speed here. With the speed increase we can comfortably solve over long time spans:

```
<- c(0.0,100.0)
tspan <- de$SDEProblem(f,g,u0,tspan,p)
prob <- diffeqr::jitoptimize_sde(de,prob)
fastprob <- de$solve(fastprob,saveat=0.005)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines') plotly
```

Let’s see how much faster the JIT-compiled version was:

```
> system.time({ for (i in 1:5){ de$solve(prob ) }})
user system elapsed146.40 0.75 147.22
> system.time({ for (i in 1:5){ de$solve(fastprob) }})
user system elapsed1.07 0.10 1.17
```

Holy Monster’s Inc. that’s about 145x faster.

In many cases you may want to share noise terms across the system.
This is known as non-diagonal noise. The DifferentialEquations.jl
SDE Tutorial explains how the matrix form of the diffusion term
corresponds to the summation style of multiple Wiener processes.
Essentially, the row corresponds to which system the term is applied to,
and the column is which noise term. So `du[i,j]`

is the
amount of noise due to the `j`

th Wiener process that’s
applied to `u[i]`

. We solve the Lorenz system with correlated
noise as follows:

```
<- JuliaCall::julia_eval("
f function f(du,u,p,t)
du[1] = 10.0*(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2] - (8/3)*u[3]
end")
<- JuliaCall::julia_eval("
g function g(du,u,p,t)
du[1,1] = 0.3u[1]
du[2,1] = 0.6u[1]
du[3,1] = 0.2u[1]
du[1,2] = 1.2u[2]
du[2,2] = 0.2u[2]
du[3,2] = 0.3u[2]
end")
<- c(1.0,0.0,0.0)
u0 <- c(0.0,100.0)
tspan <- matrix(c(0.0,0.0,0.0,0.0,0.0,0.0), nrow = 3, ncol = 2)
noise_rate_prototype
::julia_assign("u0", u0)
JuliaCall::julia_assign("tspan", tspan)
JuliaCall::julia_assign("noise_rate_prototype", noise_rate_prototype)
JuliaCall<- JuliaCall::julia_eval("SDEProblem(f, g, u0, tspan, p, noise_rate_prototype=noise_rate_prototype)")
prob <- de$solve(prob)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines') plotly
```

Here you can see that the warping effect of the noise correlations is quite visible! Note that we applied JIT compilation since it’s quite necessary for any difficult stochastic example.

A differential-algebraic equation is defined by an implicit function
`f(du,u,p,t)=0`

. All of the controls are the same as the
other examples, except here you define a function which returns the
residuals for each part of the equation to define the DAE. The initial
value `u0`

and the initial derivative `du0`

are
required, though they do not necessarily have to satisfy `f`

(known as inconsistent initial conditions). The methods will
automatically find consistent initial conditions. In order for this to
occur, `differential_vars`

must be set. This vector states
which of the variables are differential (have a derivative term), with
`false`

meaning that the variable is purely algebraic.

This example shows how to solve the Robertson equation:

```
<- function (du,u,p,t) {
f = - 0.04*u[1] + 1e4*u[2]*u[3] - du[1]
resid1 = + 0.04*u[1] - 3e7*u[2]^2 - 1e4*u[2]*u[3] - du[2]
resid2 = u[1] + u[2] + u[3] - 1.0
resid3 c(resid1,resid2,resid3)
}<- c(1.0, 0, 0)
u0 <- c(-0.04, 0.04, 0.0)
du0 <- c(0.0,100000.0)
tspan <- c(TRUE,TRUE,FALSE)
differential_vars <- de$DAEProblem(f,du0,u0,tspan,differential_vars=differential_vars)
prob <- de$solve(prob)
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = sol$t, y = ~V1, type = 'scatter', mode = 'lines') %>%
plotly::add_trace(y = ~V2) %>%
plotly::add_trace(y = ~V3) plotly
```

Additionally, an in-place JIT compiled form for `f`

can be
used to enhance the speed:

```
= JuliaCall::julia_eval("function f(out,du,u,p,t)
f out[1] = - 0.04u[1] + 1e4*u[2]*u[3] - du[1]
out[2] = + 0.04u[1] - 3e7*u[2]^2 - 1e4*u[2]*u[3] - du[2]
out[3] = u[1] + u[2] + u[3] - 1.0
end")
<- c(1.0, 0, 0)
u0 <- c(-0.04, 0.04, 0.0)
du0 <- c(0.0,100000.0)
tspan <- c(TRUE,TRUE,FALSE)
differential_vars ::julia_assign("du0", du0)
JuliaCall::julia_assign("u0", u0)
JuliaCall::julia_assign("p", p)
JuliaCall::julia_assign("tspan", tspan)
JuliaCall::julia_assign("differential_vars", differential_vars)
JuliaCall= JuliaCall::julia_eval("DAEProblem(f, du0, u0, tspan, p, differential_vars=differential_vars)")
prob = de$solve(prob) sol
```

A delay differential equation is an ODE which allows the use of
previous values. In this case, the function needs to be a JIT compiled
Julia function. It looks just like the ODE, except in this case there is
a function `h(p,t)`

which allows you to interpolate and grab
previous values.

We must provide a history function `h(p,t)`

that gives
values for `u`

before `t0`

. Here we assume that
the solution was constant before the initial time point. Additionally,
we pass `constant_lags = c(20.0)`

to tell the solver that
only constant-time lags were used and what the lag length was. This
helps improve the solver accuracy by accurately stepping at the points
of discontinuity. Together this is:

```
<- JuliaCall::julia_eval("function f(du, u, h, p, t)
f du[1] = 1.1/(1 + sqrt(10)*(h(p, t-20)[1])^(5/4)) - 10*u[1]/(1 + 40*u[2])
du[2] = 100*u[1]/(1 + 40*u[2]) - 2.43*u[2]
end")
<- JuliaCall::julia_eval("function h(p, t)
h [1.05767027/3, 1.030713491/3]
end")
<- c(1.05767027/3, 1.030713491/3)
u0 <- c(0.0, 100.0)
tspan <- c(20.0)
constant_lags ::julia_assign("u0", u0)
JuliaCall::julia_assign("tspan", tspan)
JuliaCall::julia_assign("constant_lags", tspan)
JuliaCall<- JuliaCall::julia_eval("DDEProblem(f, u0, h, tspan, constant_lags = constant_lags)")
prob <- de$solve(prob,de$MethodOfSteps(de$Tsit5()))
sol <- as.data.frame(t(sapply(sol$u,identity)))
udf ::plot_ly(udf, x = sol$t, y = ~V1, type = 'scatter', mode = 'lines') %>% plotly::add_trace(y = ~V2) plotly
```

Notice that the solver accurately is able to simulate the kink
(discontinuity) at `t=20`

due to the discontinuity of the
derivative at the initial time point! This is why declaring
discontinuities can enhance the solver accuracy.

In many cases one is interested in solving the same ODE many times over many different initial conditions and parameters. In diffeqr parlance this is called an ensemble solve. diffeqr inherits the parallelism tools of the SciML ecosystem that are used for things like automated equation discovery and acceleration. Here we will demonstrate using these parallel tools to accelerate the solving of an ensemble.

First, let’s define the JIT-accelerated Lorenz equation like before:

```
<- diffeqr::diffeq_setup()
de <- function (u,p,t){
lorenz = p[1]*(u[2]-u[1])
du1 = u[1]*(p[2]-u[3]) - u[2]
du2 = u[1]*u[2] - p[3]*u[3]
du3 c(du1,du2,du3)
}<- c(1.0,1.0,1.0)
u0 <- c(0.0,100.0)
tspan <- c(10.0,28.0,8/3)
p <- de$ODEProblem(lorenz,u0,tspan,p)
prob <- diffeqr::jitoptimize_ode(de,prob) fastprob
```

Now we use the `EnsembleProblem`

as defined on the ensemble
parallelism page of the documentation: Let’s build an ensemble by
utilizing uniform random numbers to randomize the initial conditions and
parameters:

```
<- function (prob,i,rep){
prob_func $remake(prob,u0=runif(3)*u0,p=runif(3)*p)
de
}= de$EnsembleProblem(fastprob, prob_func = prob_func, safetycopy=FALSE) ensembleprob
```

Now we solve the ensemble in serial:

`= de$solve(ensembleprob,de$Tsit5(),de$EnsembleSerial(),trajectories=10000,saveat=0.01) sol `

To add GPUs to the mix, we need to bring in DiffEqGPU. The
`diffeqr::diffeqgpu_setup()`

helper function will install
CUDA for you and bring all of the bindings into the returned object:

`<- diffeqr::diffeqgpu_setup() degpu `

Now we simply use `EnsembleGPUArray()`

to solve 10,000
ODEs on the GPU in parallel:

`<- de$solve(ensembleprob,de$Tsit5(),degpu$EnsembleGPUArray(),trajectories=10000,saveat=0.01) sol `

To see how much of an effect the parallelism has, let’s test this against R’s deSolve package. This is exactly the same problem as the documentation example for deSolve, so let’s copy that verbatim and then add a function to do the ensemble generation:

```
library(deSolve)
<- function(t, state, parameters) {
Lorenz with(as.list(c(state, parameters)), {
<- a * X + Y * Z
dX <- b * (Y - Z)
dY <- -X * Y + c * Y - Z
dZ list(c(dX, dY, dZ))
})
}
<- c(a = -8/3, b = -10, c = 28)
parameters <- c(X = 1, Y = 1, Z = 1)
state <- seq(0, 100, by = 0.01)
times <- ode(y = state, times = times, func = Lorenz, parms = parameters)
out
<- function (i){
lorenz_solve <- c(X = runif(1), Y = runif(1), Z = runif(1))
state <- c(a = -8/3 * runif(1), b = -10 * runif(1), c = 28 * runif(1))
parameters <- ode(y = state, times = times, func = Lorenz, parms = parameters)
out }
```

Using `lapply`

to generate the ensemble we get:

```
> system.time({ lapply(1:1000,lorenz_solve) })
user system elapsed
225.81 0.46 226.63
```

Now let’s see how the JIT-accelerated serial Julia version stacks up against that:

```
> system.time({ de$solve(ensembleprob,de$Tsit5(),de$EnsembleSerial(),trajectories=1000,saveat=0.01) })
user system elapsed
2.75 0.30 3.08
```

Julia is already about 73x faster than the pure R solvers here! Now let’s add GPU-acceleration to the mix:

```
> system.time({ de$solve(ensembleprob,de$Tsit5(),degpu$EnsembleGPUArray(),trajectories=1000,saveat=0.01) })
user system elapsed
1.33 1.57 2.93
```

That’s only around 2x faster. But the GPU acceleartion is made for massively parallel problems, so let’s up the trajectories a bit. We will not use more trajectories from R because that would take too much computing power, so let’s see what happens to the Julia serial and GPU at 10,000 trajectories:

```
> system.time({ de$solve(ensembleprob,de$Tsit5(),de$EnsembleSerial(),trajectories=10000,saveat=0.01) })
user system elapsed
35.02 4.19 39.25
```

```
> system.time({ de$solve(ensembleprob,de$Tsit5(),degpu$EnsembleGPUArray(),trajectories=10000,saveat=0.01) })
user system elapsed
12.03 3.57 15.60
```

To compare this to the pure Julia code:

```
using OrdinaryDiffEq, DiffEqGPU
function lorenz(du,u,p,t)
@inbounds begin
1] = p[1]*(u[2]-u[1])
du[2] = u[1]*(p[2]-u[3]) - u[2]
du[3] = u[1]*u[2] - p[3]*u[3]
du[end
nothing
end
= Float32[1.0;1.0;1.0]
u0 = (0.0f0,100.0f0)
tspan = [10.0f0,28.0f0,8/3f0]
p = ODEProblem(lorenz,u0,tspan,p)
prob = (prob,i,repeat) -> remake(prob,u0=rand(Float32,3).*u0,p=rand(Float32,3).*p)
prob_func = EnsembleProblem(prob, prob_func = prob_func, safetycopy=false)
monteprob @time sol = solve(monteprob,Tsit5(),EnsembleGPUArray(),trajectories=10_000,saveat=0.01f0)
# 9.444439 seconds (22.96 M allocations: 6.464 GiB, 44.53% gc time)
```

which is more than an order of magnitude faster for computing 10,000
trajectories, note that the major factors are that we cannot define
32-bit floating point values from R and the `prob_func`

for
generating the initial conditions and parameters is a major bottleneck
since this function is written in R.

To see how this scales in Julia, let’s take it to insane heights. First, let’s reduce the amount we’re saving:

```
@time sol = solve(monteprob,Tsit5(),EnsembleGPUArray(),trajectories=10_000,saveat=1.0f0)
# 0.801215 seconds (1.66 M allocations: 133.846 MiB)
```

This highlights that controlling memory pressure is key with GPU usage: you will get much better performance when requiring less saved points on the GPU.

```
@time sol = solve(monteprob,Tsit5(),EnsembleGPUArray(),trajectories=100_000,saveat=1.0f0)
# 1.871536 seconds (6.66 M allocations: 919.521 MiB, 2.48% gc time)
```

compared to serial:

```
@time sol = solve(monteprob,Tsit5(),EnsembleSerial(),trajectories=100_000,saveat=1.0f0)
# 22.136743 seconds (16.40 M allocations: 1.628 GiB, 42.98% gc time)
```

And now we start to see that scaling power! Let’s solve 1 million trajectories:

```
@time sol = solve(monteprob,Tsit5(),EnsembleGPUArray(),trajectories=1_000_000,saveat=1.0f0)
# 25.234710 seconds (56.53 M allocations: 8.579 GiB, 51.61% gc time)
```

For reference, let’s look at deSolve with the change to only save that much:

```
<- seq(0, 100, by = 1.0)
times <- function (i){
lorenz_solve <- c(X = runif(1), Y = runif(1), Z = runif(1))
state <- c(a = -8/3 * runif(1), b = -10 * runif(1), c = 28 * runif(1))
parameters <- ode(y = state, times = times, func = Lorenz, parms = parameters)
out
}
system.time({ lapply(1:1000,lorenz_solve) })
```

The GPU version is solving 1000x as many trajectories, 2x as fast! So conclusion, if you need the most speed, you may want to move to the Julia version to get the most out of your GPU due to Float32’s, and when using GPUs make sure it’s a problem with a relatively average or low memory pressure, and these methods will give orders of magnitude acceleration compared to what you might be used to.