The demonstration shown above is Navier-Stokes equation learned by the
MarkovNeuralOperator with only one time step information. Example can be found in
The package can be installed with the Julia package manager. From the Julia REPL, type
] to enter the Pkg REPL mode and run:
pkg> add NeuralOperators
model = Chain( # lift (d + 1)-dimensional vector field to n-dimensional vector field # here, d == 1 and n == 64 Dense(2, 64), # map each hidden representation to the next by integral kernel operator OperatorKernel(64=>64, (16, ), FourierTransform, gelu), OperatorKernel(64=>64, (16, ), FourierTransform, gelu), OperatorKernel(64=>64, (16, ), FourierTransform, gelu), OperatorKernel(64=>64, (16, ), FourierTransform), # project back to the scalar field of interest space Dense(64, 128, gelu), Dense(128, 1), )
Or one can just call:
model = FourierNeuralOperator( ch=(2, 64, 64, 64, 64, 64, 128, 1), modes=(16, ), σ=gelu )
And then train as a Flux model.
loss(𝐱, 𝐲) = l₂loss(model(𝐱), 𝐲) opt = Flux.Optimiser(WeightDecay(1f-4), Flux.ADAM(1f-3)) Flux.@epochs 50 Flux.train!(loss, params(model), data, opt)
# tuple of Ints for branch net architecture and then for trunk net, # followed by activations for branch and trunk respectively model = DeepONet((32, 64, 72), (24, 64, 72), σ, tanh)
Or specify branch and trunk as separate
Chain from Flux and pass to
branch = Chain(Dense(32, 64, σ), Dense(64, 72, σ)) trunk = Chain(Dense(24, 64, tanh), Dense(64, 72, tanh)) model = DeepONet(branch, trunk)
You can again specify loss, optimization and training parameters just as you would for a simple neural network with Flux.
loss(xtrain, ytrain, sensor) = Flux.Losses.mse(model(xtrain, sensor), ytrain) evalcb() = @show(loss(xval, yval, grid)) learning_rate = 0.001 opt = ADAM(learning_rate) parameters = params(model) Flux.@epochs 400 Flux.train!(loss, parameters, [(xtrain, ytrain, grid)], opt, cb=evalcb)
A more complete example using DeepONet architecture to solve Burgers' equation can be found in the examples.