Please install directly from repo ]add https://github.com/paulxshen/Jello.jl. The general registry version is lagging
We design parameter efficient, fully differentiable, length scale controlled geometry generators for topology optimization & generative inverse design. We use either a real space interpolation grid basis or a Fourier domain basis. The interpolation algorithm in between a grid of knots whose regular spacing controls the length scale . The Fourier algorithm approximately bound length scales in any dimension by deriving real space geometry from a compact Fourier k-space of spatial frequencies via the inverse Fourier transform. Both algorithms may be combined with real space morphological filtering which exactly eliminate undesirable thin features, close spacings and tight bends that hamper manufacturability or induce checkerboard instability. In both, Adjustable sigmoid nonlinearity is used to control edge sharpness and induce stable bounded values and adjoint gradients without an extraneous non-binary density penalty function.
using Random, GLMakie, LinearAlgebra
using Jello
fig2d = Figure()
l = 50
Random.seed!(1)
alg = :interpolation
nbasis = 4
contrast = 1
rmin = nothing
m = Blob(l, l; alg, nbasis, contrast)
heatmap(fig2d[1, 1], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = $rmin"))
Random.seed!(1)
alg = :fourier
nbasis = 4
contrast = 1
rmin = nothing
m = Blob(l, l; alg, nbasis, contrast)
heatmap(fig2d[1, 2], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = $rmin"))
Random.seed!(1)
alg = :interpolation
nbasis = 6
m = Blob(l, l; nbasis, contrast)
heatmap(fig2d[2, 1], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = $rmin"))
Random.seed!(1)
contrast = 20
m = Blob(l, l; nbasis, contrast)
heatmap(fig2d[2, 2], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = $rmin"))
# Random.seed!(1)
# rmin = :auto
# m = Blob(l, l; nbasis, contrast, rmin)
# heatmap(fig2d[2, 3], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = :$rmin"))
Random.seed!(1)
rmin = 3
m = Blob(l, l; nbasis, contrast, rmin)
heatmap(fig2d[2, 3], m(); axis=(; title="$l x $l\nalg = :$alg\nnbasis = $nbasis\ncontrast = $contrast\nrmin = $rmin"))
save("samples2d.png", fig2d)
fig2d
Random.seed!(1)
l = 40
nbasis = 4
contrast = 20
m = Blob(l, l, l; nbasis, contrast,)
fig3d = volume(m(); algorithm=:absorption, axis=(; type=Axis3, title="$l x $l x $l, alg = :$alg, nbasis = $nbasis, contrast = $contrast, rmin = $rmin"))
save("samples3d.png", fig3d)
fig3d
In real applications, our geometry generator would interface with a FEM or FDM solver that computes a loss function against a target metric. For gradient based adjoint optimization, the solver needs to be amenable to automatic differentiation or have hard coded adjoints. For the sake of testing Jello.jl, we pretend we know the optimal geometry (eg circle) and verify that Jello.jl can reach it through gradient descent.
using Test, Random, Flux, GLMakie, LinearAlgebra, StatsBase
using Flux: gradient, withgradient
using AbbreviatedStackTraces
using Jello
Random.seed!(1)
l = 30
y = float.([norm([x, y] - [l, l] / 2) < l / 4 for x = 1:l, y = 1:l]) # circle
model = Blob(l, l; alg=:interpolation, nbasis=10, contrast=10,)
# model = Blob(l, l; alg=:fourier, nbasis=10, contrast=10,)
iterations = 20
fig = Figure()
empty!(fig)
aspect = 1
heatmap(fig[1, 1], model(), axis=(; aspect, title="start of training"))
loss(model) = mean(abs, y - model())
# train
opt = Adam(0.5)
opt_state = Flux.setup(opt, model)
for i = 1:iterations
l, (dldm,) = withgradient(loss, model)
Flux.update!(opt_state, model, dldm)
i % 1 == 0 && println("$i $l")
end
heatmap(fig[2, 1], model(), axis=(; title="Flux.Adam $iterations steps", aspect))
save("pic.png", fig)
display(fig)
Open to collaboration and consulting. LMK if you need the package in Pytorch or JAX, or wish to try it out on an adjoint FEM solver. We can also interface with solvers in C or other languages by passing the Jacobian.
Consider sponsoring this package if you found this repo helpful. Feel free to request features or contribute PRs :)