Join the chat at https://gitter.im/JuliaDiffEq/Lobby Build Status Build status GitlabCI

DiffEqFlux.jl fuses the world of differential equations with machine learning by helping users put diffeq solvers into neural networks. This package utilizes DifferentialEquations.jl and Flux.jl as its building blocks to support research in Scientific Machine Learning and neural differential equations in traditional machine learning.


Problem Domain

DiffEqFlux.jl is not just for neural ordinary differential equations. DiffEqFlux.jl is for universal differential equations. For an overview of the topic with applications, consult the paper Universal Differential Equations for Scientific Machine Learning

As such, it is the first package to support and demonstrate:

  • Stiff universal ordinary differential equations (universal ODEs)
  • Universal stochastic differential equations (universal SDEs)
  • Universal delay differential equations (universal DDEs)
  • Universal partial differential equations (universal PDEs)
  • Universal jump stochastic differential equations (universal jump diffusions)
  • Hybrid universal differential equations (universal DEs with event handling)

with high order, adaptive, implicit, GPU-accelerated, Newton-Krylov, etc. methods. For examples, please refer to the release blog post. Additional demonstrations, like neural PDEs and neural jump SDEs, can be found at this blog post (among many others!).

Do not limit yourself to the current neuralization. With this package, you can explore various ways to integrate the two methodologies:

  • Neural networks can be defined where the “activations” are nonlinear functions described by differential equations.
  • Neural networks can be defined where some layers are ODE solves
  • ODEs can be defined where some terms are neural networks
  • Cost functions on ODEs can define neural networks


If you use DiffEqFlux.jl or are influenced by its ideas for expanding beyond neural ODEs, please cite:

  author    = {Christopher Rackauckas and
               Mike Innes and
               Yingbo Ma and
               Jesse Bettencourt and
               Lyndon White and
               Vaibhav Dixit},
  title     = {DiffEqFlux.jl - {A} Julia Library for Neural Differential Equations},
  journal   = {CoRR},
  volume    = {abs/1902.02376},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.02376},
  archivePrefix = {arXiv},
  eprint    = {1902.02376},
  timestamp = {Tue, 21 May 2019 18:03:36 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1902-02376},
  bibsource = {dblp computer science bibliography, https://dblp.org}

Example Usage

For an overview of what this package is for, see this blog post.

Optimizing parameters of an ODE for an Optimal Control problem

First let's create a Lotka-Volterra ODE using DifferentialEquations.jl. For more details, see the DifferentialEquations.jl documentation

using DifferentialEquations, Flux, Optim, DiffEqFlux
function lotka_volterra(du,u,p,t)
  x, y = u
  α, β, δ, γ = p
  du[1] = dx = α*x - β*x*y
  du[2] = dy = -δ*y + γ*x*y
u0 = [1.0,1.0]
tspan = (0.0,10.0)
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(lotka_volterra,u0,tspan,p)
sol = solve(prob,Tsit5())
using Plots

LV Solution Plot

Next we define a single layer neural network that using the AD-compatible concrete_solve function function that takes the parameters and an initial condition and returns the solution of the differential equation as a DiffEqArray (same array semantics as the standard differential equation solution object but without the interpolations).

function predict_adjoint(p) # Our 1-layer neural network

Next we choose a loss function. Our goal will be to find parameter that make the Lotka-Volterra solution constant x(t)=1, so we defined our loss as the squared distance from 1. Note that when using sciml_train, the first return is the loss value, and the other returns are sent to the callback for monitoring convergence.

function loss_adjoint(p)
  prediction = predict_adjoint(p)
  loss = sum(abs2,x-1 for x in prediction)

Lastly, we use the sciml_train function to train the parameters using BFGS to arrive at parameters which optimize for our goal. sciml_train allows defining a callback that will be called at each step of our training loop. It takes in the current parameter vector and the returns of the last call to the loss function. We will display the current loss and make a plot of the current situation:

cb = function (p,l,pred) #callback function to observe training
  # using `remake` to re-create our `prob` with current parameters `p`
  return false # Tell it to not halt the optimization. If return true, then optimization stops

# Display the ODE with the initial parameter values.

res = DiffEqFlux.sciml_train(loss_adjoint, p, BFGS(initial_stepnorm = 0.0001), cb = cb)
* Status: failure (objective increased between iterations) (line search failed)

* Candidate solution
   Minimizer: [1.44e+00, 1.44e+00, 2.54e+00,  ...]
   Minimum:   1.993849e-06

* Found with
   Algorithm:     BFGS
   Initial Point: [1.50e+00, 1.00e+00, 3.00e+00,  ...]

* Convergence measures
   |x - x'|               = 1.27e-09  0.0e+00
   |x - x'|/|x'|          = 4.98e-10  0.0e+00
   |f(x) - f(x')|         = 1.99e-12  0.0e+00
   |f(x) - f(x')|/|f(x')| = 1.00e-06  0.0e+00
   |g(x)|                 = 5.74e-03  1.0e-08

* Work counters
   Seconds run:   6  (vs limit Inf)
   Iterations:    6
   f(x) calls:    83
   ∇f(x) calls:   83

In just seconds we found parameters which give a loss of 1e-6! We can get the final loss with res.minimum, and get the optimal parameters with res.minimizer. For example, we can plot the final outcome and show that we solved the control problem and successfully found parameters to make the ODE solution constant:


Here's an old video showing the use of the ADAM optimizer:

Flux ODE Training Animation

Using Other Differential Equations

Other differential equation problem types from DifferentialEquations.jl are supported. For example, we can build a layer with a delay differential equation like:

function delay_lotka_volterra(du,u,h,p,t)
  x, y = u
  α, β, δ, γ = p
  du[1] = dx = (α - β*y)*h(p,t-0.1)[1]
  du[2] = dy = (δ*x - γ)*y
h(p,t) = ones(eltype(p),2)
u0 = [1.0,1.0]
prob = DDEProblem(delay_lotka_volterra,u0,h,(0.0,10.0),constant_lags=[0.1])

p = [2.2, 1.0, 2.0, 0.4]
function predict_dde(p)
loss_dde(p) = sum(abs2,x-1 for x in predict_dde(p))

Notice that we chose sensealg=TrackerAdjoint() to utilize the Tracker.jl reverse-mode to handle the delay differential equation.

Or we can use a stochastic differential equation. Here we demonstrate sensealg=ForwardDiffSensitivity() for forward-mode automatic differentiation of a small stochastic differential equation:

function lotka_volterra_noise(du,u,p,t)
  du[1] = 0.1u[1]
  du[2] = 0.1u[2]
u0 = [1.0,1.0]
prob = SDEProblem(lotka_volterra,lotka_volterra_noise,u0,(0.0,10.0))

p = [2.2, 1.0, 2.0, 0.4]
function predict_sde(p)
loss_sde(p) = sum(abs2,x-1 for x in predict_sde(p))

For this training process, because the loss function is stochastic, we will use the ADAM optimizer from Flux.jl. The sciml_train function is the same as before. However, to speed up the training process, we will use a global counter so that way we only plot the current results every 10 iterations. This looks like:

iter = 0
cb = function (p,l)
  global iter
  if iter%10 == 0
  iter += 1
  return false

# Display the ODE with the current parameter values.

DiffEqFlux.sciml_train(loss_sde, p, ADAM(0.1), cb = cb, maxiters = 100)

SDE NN Animation

Neural Ordinary Differential Equations

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)), the best way to define a neural ODE by hand would be to use non-mutating adjoints, which looks like:

p,re = Flux.destructure(model)
dudt_(u,p,t) = re(p)(u)
prob = ODEProblem(dudt_,x,tspan,p)
my_neural_ode_prob = concrete_solve(prob,Tsit5(),u0,p,args...;kwargs...)

(Flux.restructure and Flux.destructure are helper functions which transform the neural network to use parameters p)

A convenience function which handles all of the details is NeuralODE. To use NeuralODE, you give it the initial condition, the internal neural network model to use, the timespan to solve on, and any ODE solver arguments. For example, this neural ODE would be defined as:

tspan = (0.0f0,25.0f0)
n_ode = NeuralODE(model,tspan,Tsit5(),saveat=0.1)

where here we made it a layer that takes in the initial condition and spits out an array for the time series saved at every 0.1 time steps.

Training a Neural Ordinary Differential Equation

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

using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots

u0 = Float32[2.; 0.]
datasize = 30
tspan = (0.0f0,1.5f0)

function trueODEfunc(du,u,p,t)
    true_A = [-0.1 2.0; -2.0 -0.1]
    du .= ((u.^3)'true_A)'
t = range(tspan[1],tspan[2],length=datasize)
prob = ODEProblem(trueODEfunc,u0,tspan)
ode_data = Array(solve(prob,Tsit5(),saveat=t))

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,
n_ode = NeuralODE(dudt2,tspan,Tsit5(),saveat=t)

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

dudt2 = Chain(x -> x.^3,
n_ode = NeuralODE(dudt2,tspan,Tsit5(),saveat=t)

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_n_ode(p)

function loss_n_ode(p)
    pred = predict_n_ode(p)
    loss = sum(abs2,ode_data .- pred)

loss_n_ode(n_ode.p) # n_ode.p stores the initial parameters of the neural ODE

and then train the neural network to learn the ODE:

cb = function (p,l,pred;doplot=false) #callback function to observe training
  # plot current prediction against data
  if doplot
    pl = scatter(t,ode_data[1,:],label="data")
  return false

# Display the ODE with the initial parameter values.

res1 = DiffEqFlux.sciml_train(loss_n_ode, n_ode.p, ADAM(0.05), cb = cb, maxiters = 300)
res2 = DiffEqFlux.sciml_train(loss_n_ode, res1.minimizer, LBFGS(), cb = cb)

# result is res2 as an Optim.jl object
# res2.minimizer are the best parameters
# res2.minimum is the best loss
* 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
   Algorithm:     ADAM
   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

* 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

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).

Use with GPUs

Note that the differential equation solvers will run on the GPU if the initial condition is a GPU array. Thus for example, we can define a neural ODE by hand that runs on the GPU:

u0 = Float32[2.; 0.] |> gpu
dudt = Chain(Dense(2,50,tanh),Dense(50,2)) |> gpu

p,re = DiffEqFlux.destructure(model)
dudt_(u,p,t) = re(p)(u)
prob = ODEProblem(ODEfunc, u0,tspan, p)

# Runs on a GPU
sol = solve(prob,Tsit5(),saveat=0.1)

and concrete_solve works similarly. Or we can directly use the neural ODE layer function, like:

n_ode = NeuralODE(gpu(dudt2),tspan,Tsit5(),saveat=0.1)

Universal Differential Equations

You can also mix a known differential equation and a neural differential equation, so that the parameters and the neural network are estimated simultaneously!

Universal Differential Equations for Neural Optimal Control

Here's an example of doing this with both reverse-mode autodifferentiation and with adjoints. We will assume that we know the dynamics of the second equation (linear dynamics), and our goal is to find a neural network that will control the second equation to stay close to 1.

using DiffEqFlux, Flux, Optim, OrdinaryDiffEq

u0 = Float32(1.1)
tspan = (0.0f0,25.0f0)

ann = FastChain(FastDense(2,16,tanh), FastDense(16,16,tanh), FastDense(16,1))
p1 = initial_params(ann)
p2 = Float32[0.5,-0.5]
p3 = [p1;p2]
θ = Float32[u0;p3]

function dudt_(du,u,p,t)
    x, y = u
    du[1] = ann(u,p[1:length(p1)])[1]
    du[2] = p[end-1]*y + p[end]*x
prob = ODEProblem(dudt_,u0,tspan,p3)

function predict_adjoint(θ)
loss_adjoint(θ) = sum(abs2,predict_adjoint(θ)[2,:].-1)
l = loss_adjoint(θ)

cb = function (θ,l)
  return false

# Display the ODE with the current parameter values.

loss1 = loss_adjoint(θ)
res = DiffEqFlux.sciml_train(loss_adjoint, θ, BFGS(initial_stepnorm=0.01), cb = cb)
* Status: success

* Candidate solution
   Minimizer: [1.00e+00, 4.33e-02, 3.72e-01,  ...]
   Minimum:   6.572520e-13

* Found with
   Algorithm:     BFGS
   Initial Point: [1.10e+00, 4.18e-02, 3.64e-01,  ...]

* Convergence measures
   |x - x'|               = 0.00e+00  0.0e+00
   |x - x'|/|x'|          = 0.00e+00  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)|                 = 5.45e-06  1.0e-08

* Work counters
   Seconds run:   8  (vs limit Inf)
   Iterations:    23
   f(x) calls:    172
   ∇f(x) calls:   172

Notice that in just 23 iterations or 8 seconds we get to a minimum of 7e-13, successfully solving the nonlinear optimal control problem.

Neural Differential Equations for Non-ODEs: Neural SDEs, Neural DDEs, etc.

With neural stochastic differential equations, there is once again a helper form neural_dmsde which can be used for the multiplicative noise case (consult the layers API documentation, or this full example using the layer function).

However, since there are far too many possible combinations for the API to support, in many cases you will want to performantly define neural differential equations for non-ODE systems from scratch. For these systems, it is generally best to use TrackerAdjoint with non-mutating (out-of-place) forms. For example, the following defines a neural SDE with neural networks for both the drift and diffusion terms:

dudt_(u,p,t) = model(u)
g(u,p,t) = model2(u)
prob = SDEProblem(dudt_,g,x,tspan,nothing)

where model and model2 are different neural networks. The same can apply to a neural delay differential equation. Its out-of-place formulation is f(u,h,p,t). Thus for example, if we want to define a neural delay differential equation which uses the history value at p.tau in the past, we can define:

dudt_(u,h,p,t) = model([u;h(t-p.tau)])
prob = DDEProblem(dudt_,u0,h,tspan,nothing)

Neural SDE Example

First let's build training data from the same example as the neural ODE:

using Flux, DiffEqFlux, StochasticDiffEq, Plots, DiffEqBase.EnsembleAnalysis, Statistics

u0 = Float32[2.; 0.]
datasize = 30
tspan = (0.0f0,1.0f0)

function trueSDEfunc(du,u,p,t)
    true_A = [-0.1 2.0; -2.0 -0.1]
    du .= ((u.^3)'true_A)'
t = range(tspan[1],tspan[2],length=datasize)
mp = Float32[0.2,0.2]
function true_noise_func(du,u,p,t)
    du .= mp.*u
prob = SDEProblem(trueSDEfunc,true_noise_func,u0,tspan)

For our dataset we will use DifferentialEquations.jl's parallel ensemble interface to generate data from the average of 10000 runs of the SDE:

# Take a typical sample from the mean
ensemble_prob = EnsembleProblem(prob)
ensemble_sol = solve(ensemble_prob,SOSRI(),trajectories = 10000)
ensemble_sum = EnsembleSummary(ensemble_sol)
sde_data,sde_data_vars = Array.(timeseries_point_meanvar(ensemble_sol,t))

Now we build a neural SDE. For simplicity we will use the NeuralDSDE neural SDE with diagonal noise layer function:

drift_dudt = FastChain((x,p) -> x.^3,
diffusion_dudt = FastChain(FastDense(2,2))
n_sde = NeuralDSDE(drift_dudt,diffusion_dudt,tspan,SOSRI(),saveat=t,reltol=1e-1,abstol=1e-1)

Let's see what that looks like:

pred = n_sde(u0) # Get the prediction using the correct initial condition
drift_(u,p,t) = drift_dudt(u,p[1:n_sde.len])
diffusion_(u,p,t) = diffusion_dudt(u,p[(n_sde.len+1):end])
nprob = SDEProblem(drift_,diffusion_,u0,(0.0f0,1.2f0),n_sde.p)

ensemble_nprob = EnsembleProblem(nprob)
ensemble_nsol = solve(ensemble_nprob,SOSRI(),trajectories = 100, saveat = t)
ensemble_nsum = EnsembleSummary(ensemble_nsol)
p1 = plot(ensemble_nsum, title = "Neural SDE: Before Training")

Now just as with the neural ODE we define a loss function that calculates the mean and variance from n runs at each time point and uses the distance from the data values:

function predict_n_sde(p)
function loss_n_sde(p;n=100)
  samples = [predict_n_sde(p) for i in 1:n]
  means = reshape(mean.([[samples[i][j] for i in 1:length(samples)] for j in 1:length(samples[1])]),size(samples[1])...)
  vars = reshape(var.([[samples[i][j] for i in 1:length(samples)] for j in 1:length(samples[1])]),size(samples[1])...)
  loss = sum(abs2,sde_data - means) + sum(abs2,sde_data_vars - vars)

cb = function (p,loss,means,vars) #callback function to observe training
  # loss against current data
  # plot current prediction against data
  pl = scatter(t,sde_data[1,:],yerror = sde_data_vars[1,:],label="data")
  scatter!(pl,t,means[1,:],ribbon = vars[1,:], label="prediction")
  return false

# Display the SDE with the initial parameter values.

Now we train using this loss function. We can pre-train a little bit using a smaller n and then decrease it after it has had some time to adjust towards the right mean behavior:

opt = ADAM(0.025)
res1 = DiffEqFlux.sciml_train((p)->loss_n_sde(p,n=10),  n_sde.p, opt, cb = cb, maxiters = 100)
res2 = DiffEqFlux.sciml_train((p)->loss_n_sde(p,n=100), res1.minimizer, opt, cb = cb, maxiters = 100)

And now we plot the solution to an ensemble of the trained neural SDE:

samples = [predict_n_sde(res2.minimizer) for i in 1:1000]
means = reshape(mean.([[samples[i][j] for i in 1:length(samples)] for j in 1:length(samples[1])]),size(samples[1])...)
vars = reshape(var.([[samples[i][j] for i in 1:length(samples)] for j in 1:length(samples[1])]),size(samples[1])...)

p2 = scatter(t,sde_data',yerror = sde_data_vars',label="data", title = "Neural SDE: After Training", xlabel="Time")
plot!(p2,t,means',lw = 8,ribbon = vars', label="prediction")



Try this with GPUs as well!

Enforcing Physical Constraints with Singular Mass Matrices

As shown in the stiff ODE tutorial, differential-algebraic equations (DAEs) can be used to impose physical constraints. One way to define a DAE is through an ODE with a singular mass matrix. For example, if we make Mu = f(u) where the last row of M is all zeros, then we have a constraint defined by the right hand side. Using NeuralODEMM, we can use this to define a neural ODE where the sum of all 3 terms must add to one. An example of this is as follows:

using Flux, DiffEqFlux, OrdinaryDiffEq, Optim, Test
#A desired MWE for now, not a test yet.
function f(du,u,p,t)
    y₁,y₂,y₃ = u
    k₁,k₂,k₃ = p
    du[1] = -k₁*y₁ + k₃*y₂*y₃
    du[2] =  k₁*y₁ - k₃*y₂*y₃ - k₂*y₂^2
    du[3] =  y₁ + y₂ + y₃ - 1
u₀ = [1.0, 0, 0]
M = [1. 0  0
     0  1. 0
     0  0  0]
tspan = (0.0,1.0)
p = [0.04,3e7,1e4]
func = ODEFunction(f,mass_matrix=M)
prob = ODEProblem(func,u₀,tspan,p)
sol = solve(prob,Rodas5(),saveat=0.1)

dudt2 = FastChain(FastDense(3,64,tanh),FastDense(64,2))
ndae = NeuralODEMM(dudt2, (u,p,t) -> [u[1] + u[2] + u[3] - 1], tspan, M, Rodas5(autodiff=false),saveat=0.1)

function predict_n_dae(p)
function loss(p)
    pred = predict_n_dae(p)
    loss = sum(abs2,sol .- pred)

cb = function (p,l,pred) #callback function to observe training
  return false

l1 = first(loss(ndae.p))
res = DiffEqFlux.sciml_train(loss, ndae.p, BFGS(initial_stepnorm = 0.001),
                             cb = cb, maxiters = 100)
@test res.minimum < l1

This is a highly stiff problem, making the fitting difficult, but we have manually imposed that sum constraint via (u,p,t) -> [u[1] + u[2] + u[3] - 1], making the fitting easier.

Neural Jump Diffusions (Neural Jump SDE) and Neural Partial Differential Equations (Neural PDEs)

For the sake of not having a never-ending documentation of every single combination of CPU/GPU with every layer and every neural differential equation, we will end here. But you may want to consult this blog post which showcases defining neural jump diffusions and neural partial differential equations.

API Documentation

Neural DE Layer Functions

  • NeuralODE(model,tspan,solver,args...;kwargs...) defines a neural ODE layer where model is a Flux.jl model, tspan is the time span to integrate, and the rest of the arguments are passed to the ODE solver.
  • NeuralODEMM(model,constraints,tspan,mass_matrix,args...;kwargs...) defines a neural ODE layer with a mass matrix, i.e. Mu=[model(u);constraints(u,p,t)] where the constraints cover the rank-deficient area of the mass matrix (i.e., the constraints of a differential-algebraic equation). Thus the mass matrix is allowed to be singular.
  • NeuralDSDE(model1,model2,tspan,solver,args...;kwargs...) defines a neural SDE layer where model1 is a Flux.jl for the drift equation, model2 is a Flux.jl model for the diffusion equation, tspan is the time span to integrate, and the rest of the arguments are passed to the SDE solver. The noise is diagonal, i.e. it assumes a vector output and performs model2(u) .* dW against a dW matching the number of states.
  • NeuralSDE(model1,model2,tspan,nbrown,solver,args...;kwargs...) defines a neural SDE layer where model1 is a Flux.jl for the drift equation, model2 is a Flux.jl model for the diffusion equation, tspan is the time span to integrate, nbrown is the number of Brownian motions, and the rest of the arguments are passed to the SDE solver. The model is multiplicative, i.e. it's interpreted as model2(u) * dW, and so the return of model2 should be an appropriate matrix for performing this multiplication, i.e. the size of its output should be length(x) x nbrown.
  • NeuralCDDE(model,tspan,lags,solver,args...;kwargs...)defines a neural DDE layer where model is a Flux.jl model, tspan is the time span to integrate, lags is the lagged values to use in the predictor, and the rest of the arguments are passed to the ODE solver. The model should take in a vector that concatenates the lagged states, i.e. [u(t);u(t-lags[1]);...;u(t-lags[end])]


A raw ODE solver benchmark showcases a 50,000x performance advantage over torchdiffeq on small ODEs.