# Newton and Hessian-Free Newton-Krylov with Second Order Adjoint Sensitivity Analysis

In many cases it may be more optimal or more stable to fit using second order Newton-based optimization techniques. Since DiffEqSensitivity.jl provides second order sensitivity analysis for fast Hessians and Hessian-vector products (via forward-over-reverse), we can utilize these in our neural/universal differential equation training processes.

`sciml_train`

is setup to automatically use second order sensitivity analysis methods if a second order optimizer is requested via Optim.jl. Thus `Newton`

and `NewtonTrustRegion`

optimizers will use a second order Hessian-based optimization, while `KrylovTrustRegion`

will utilize a Krylov-based method with Hessian-vector products (never forming the Hessian) for large parameter optimizations.

```
using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots
u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan[1], tspan[2], 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)
p = prob_neuralode.p
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 to observe training
list_plots = []
iter = 0
cb = function (p, l, pred; doplot = false)
global list_plots, iter
if iter == 0
list_plots = []
end
iter += 1
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
push!(list_plots, plt)
if doplot
display(plot(plt))
end
return l < 0.01
end
pstart = DiffEqFlux.sciml_train(loss_neuralode, p, ADAM(0.01), cb=cb, maxiters = 100).minimizer
pmin = DiffEqFlux.sciml_train(loss_neuralode, pstart, NewtonTrustRegion(), cb=cb, maxiters = 200)
pmin = DiffEqFlux.sciml_train(loss_neuralode, pstart, Optim.KrylovTrustRegion(), cb=cb, maxiters = 200)
```

Note that we do not demonstrate `Newton()`

because we have not found a single case where it is competitive with the other two methods. `KrylovTrustRegion()`

is generally the fastest due to its use of Hessian-vector products.