# Neural Ordinary Differential Equations with sciml_train

We can use DiffEqFlux.jl to define, solve, and train neural ordinary differential equations. A neural ODE is an ODE where a neural network defines its derivative function. Thus for example, with the multilayer perceptron neural network Chain(Dense(2, 50, tanh), Dense(50, 2)),

## Copy-Pasteable Code

Before getting to the explanation, here's some code to start with. We will follow wil a full explanation of the definition and training process:

using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots

u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan, tspan, length = datasize)

function trueODEfunc(du, u, p, t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end

prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))

dudt2 = FastChain((x, p) -> x.^3,
FastDense(2, 50, tanh),
FastDense(50, 2))
prob_neuralode = NeuralODE(dudt2, tspan, Tsit5(), saveat = tsteps)

function predict_neuralode(p)
Array(prob_neuralode(u0, p))
end

function loss_neuralode(p)
pred = predict_neuralode(p)
loss = sum(abs2, ode_data .- pred)
return loss, pred
end

callback = function (p, l, pred; doplot = true)
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
if doplot
display(plot(plt))
end
return false
end

result_neuralode = DiffEqFlux.sciml_train(loss_neuralode, prob_neuralode.p,
maxiters = 300)

result_neuralode2 = DiffEqFlux.sciml_train(loss_neuralode,
result_neuralode.minimizer,
LBFGS(),
cb = callback,
allow_f_increases = false) ## Explanation

Let's get a time series array from the Lotka-Volterra equation as data:

using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots

u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan, tspan, length = datasize)

function trueODEfunc(du, u, p, t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end

prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))

Now let's define a neural network with a NeuralODE layer. First we define the layer. Here we're going to use FastChain, which is a faster neural network structure for NeuralODEs:

dudt2 = FastChain((x, p) -> x.^3,
FastDense(2, 50, tanh),
FastDense(50, 2))
prob_neuralode = NeuralODE(dudt2, tspan, Tsit5(), saveat = tsteps)

Note that we can directly use Chains from Flux.jl as well, for example:

dudt2 = Chain(x -> x.^3,
Dense(2, 50, tanh),
Dense(50, 2))

In our model we used the x -> x.^3 assumption in the model. By incorporating structure into our equations, we can reduce the required size and training time for the neural network, but a good guess needs to be known!

From here we build a loss function around it. The NeuralODE has an optional second argument for new parameters which we will use to iteratively change the neural network in our training loop. We will use the L2 loss of the network's output against the time series data:

function predict_neuralode(p)
Array(prob_neuralode(u0, p))
end

function loss_neuralode(p)
pred = predict_neuralode(p)
loss = sum(abs2, ode_data .- pred)
return loss, pred
end

We define a callback function.

# Callback function to observe training
callback = function (p, l, pred; doplot = false)
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
if doplot
display(plot(plt))
end
return false
end

We then train the neural network to learn the ODE.

Here we showcase starting the optimization with ADAM to more quickly find a minimum, and then honing in on the minimum by using LBFGS. By using the two together, we are able to fit the neural ODE in 9 seconds! (Note, the timing commented out the plotting).

# Train using the ADAM optimizer
result_neuralode = DiffEqFlux.sciml_train(loss_neuralode, prob_neuralode.p,
maxiters = 300)

* Status: failure (reached maximum number of iterations)

* Candidate solution
Minimizer: [4.38e-01, -6.02e-01, 4.98e-01,  ...]
Minimum:   8.691715e-02

* Found with
Initial Point: [-3.02e-02, -5.40e-02, 2.78e-01,  ...]

* Convergence measures
|x - x'|               = NaN ≰ 0.0e+00
|x - x'|/|x'|          = NaN ≰ 0.0e+00
|f(x) - f(x')|         = NaN ≰ 0.0e+00
|f(x) - f(x')|/|f(x')| = NaN ≰ 0.0e+00
|g(x)|                 = NaN ≰ 0.0e+00

* Work counters
Seconds run:   5  (vs limit Inf)
Iterations:    300
f(x) calls:    300
∇f(x) calls:   300

We then complete the training using a different optimizer starting from where ADAM stopped. We do allow_f_increases=false to make the optimization automatically halt when near the minimum.

# Retrain using the LBFGS optimizer
result_neuralode2 = DiffEqFlux.sciml_train(loss_neuralode,
result_neuralode.minimizer,
LBFGS(),
cb = callback,
allow_f_increases = false)

* Status: success

* Candidate solution
Minimizer: [4.23e-01, -6.24e-01, 4.41e-01,  ...]
Minimum:   1.429496e-02

* Found with
Algorithm:     L-BFGS
Initial Point: [4.38e-01, -6.02e-01, 4.98e-01,  ...]

* Convergence measures
|x - x'|               = 1.46e-11 ≰ 0.0e+00
|x - x'|/|x'|          = 1.26e-11 ≰ 0.0e+00
|f(x) - f(x')|         = 0.00e+00 ≤ 0.0e+00
|f(x) - f(x')|/|f(x')| = 0.00e+00 ≤ 0.0e+00
|g(x)|                 = 4.28e-02 ≰ 1.0e-08

* Work counters
Seconds run:   4  (vs limit Inf)
Iterations:    35
f(x) calls:    336
∇f(x) calls:   336

## Usage without the layer

Note that you can equivalently define the NeuralODE by hand instead of using the layer function. With FastChain this would look like:

dudt!(u, p, t) = dudt2(u, p)
u0 = rand(2)
prob = ODEProblem(dudt!, u0, tspan, p)
my_neural_ode_prob = solve(prob, Tsit5(), args...; kwargs...)

and with Chain this would look like:

p, re = Flux.destructure(dudt2)
neural_ode_f(u, p, t) = re(p)(u)
u0 = rand(2)
prob = ODEProblem(neural_ode_f, u0, tspan, p)
my_neural_ode_prob = solve(prob, Tsit5(), args...; kwargs...)

and then one would use solve for the prediction like in other tutorials.

In total, the from scratch form looks like:

using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots

u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan, tspan, length = datasize)

function trueODEfunc(du, u, p, t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end

prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))

dudt2 = FastChain((x, p) -> x.^3,
FastDense(2, 50, tanh),
FastDense(50, 2))
neural_ode_f(u,p,t) = dudt2(u,p)
pinit = initial_params(dudt2)
prob = ODEProblem(neural_ode_f, u0, tspan, pinit)

function predict_neuralode(p)
tmp_prob = remake(prob,p=p)
Array(solve(tmp_prob,Tsit5(),saveat=tsteps))
end

function loss_neuralode(p)
pred = predict_neuralode(p)
loss = sum(abs2, ode_data .- pred)
return loss, pred
end

callback = function (p, l, pred; doplot = true)
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
if doplot
display(plot(plt))
end
return false
end

result_neuralode = DiffEqFlux.sciml_train(loss_neuralode, pinit,
allow_f_increases = false)