Fix docs: replace DomainSets.infimum/supremum with IntervalSets.leftendpoint/rightendpoint

claude · ChrisRackauckas · claude · commit a92fe61dc6b2 · 2026-01-29T20:46:32.000-05:00
DomainSets no longer exports infimum/supremum. Replace all uses
across documentation with IntervalSets.leftendpoint/rightendpoint
and add IntervalSets to docs/Project.toml.

Co-Authored-By: Chris Rackauckas &lt;accounts@chrisrackauckas.com&gt;
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -9,6 +9,7 @@ DomainSets = "5b8099bc-c8ec-5219-889f-1d9e522a28bf"
 FastGaussQuadrature = "442a2c76-b920-505d-bb47-c5924d526838"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Integrals = "de52edbc-65ea-441a-8357-d3a637375a31"
+IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
 LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
@@ -40,6 +41,7 @@ DomainSets = "0.6, 0.7"
 FastGaussQuadrature = "1"
 Flux = "0.14.17, 0.15, 0.16"
 Integrals = "4"
+IntervalSets = "0.7"
 LineSearches = "7.2"
 Lux = "1"
 LuxCUDA = "0.3.2"
diff --git a/docs/src/developer/debugging.md b/docs/src/developer/debugging.md
@@ -7,7 +7,8 @@ PDE solvers.
 
 ```julia
 using NeuralPDE, ModelingToolkit, Flux, Zygote
-import DomainSets: Interval, infimum, supremum
+import DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 # 2d wave equation, neumann boundary condition
 @parameters x, t
 @variables u(..)
diff --git a/docs/src/examples/3rd.md b/docs/src/examples/3rd.md
@@ -17,7 +17,8 @@ We will use physics-informed neural networks.
 ```@example 3rdDerivative
 using NeuralPDE, Lux, ModelingToolkit
 using Optimization, OptimizationOptimJL, OptimizationOptimisers
-import DomainSets: Interval, infimum, supremum
+import DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters x
 @variables u(..)
@@ -59,7 +60,7 @@ using Plots
 analytic_sol_func(x) = (π * x * (-x + (π^2) * (2 * x - 3) + 1) - sin(π * x)) / (π^3)
 
 dx = 0.05
-xs = [infimum(d.domain):(dx / 10):supremum(d.domain) for d in domains][1]
+xs = [leftendpoint(d.domain):(dx / 10):rightendpoint(d.domain) for d in domains][1]
 u_real = [analytic_sol_func(x) for x in xs]
 u_predict = [first(phi(x, res.u)) for x in xs]
 
diff --git a/docs/src/examples/linear_parabolic.md b/docs/src/examples/linear_parabolic.md
@@ -28,7 +28,8 @@ with a physics-informed neural network.
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimisers,
       OptimizationOptimJL, LineSearches
 using Plots
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u(..), w(..)
@@ -98,7 +99,7 @@ res = solve(prob, OptimizationOptimisers.Adam(1e-2); maxiters = 5000, callback)
 phi = discretization.phi
 
 # Analysis
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 depvars = [:u, :w]
 minimizers_ = [res.u.depvar[depvars[i]] for i in 1:length(chain)]
 
diff --git a/docs/src/examples/nonlinear_elliptic.md b/docs/src/examples/nonlinear_elliptic.md
@@ -29,7 +29,8 @@ This is done using a derivative neural network approximation.
 ```@example nonlinear_elliptic
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, Roots
 using Plots
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters x, y
 Dx = Differential(x)
@@ -106,7 +107,7 @@ res = solve(prob, BFGS(); maxiters = 100, callback)
 phi = discretization.phi
 
 # Analysis
-xs, ys = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+xs, ys = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 depvars = [:u, :w]
 minimizers_ = [res.u.depvar[depvars[i]] for i in 1:2]
 
diff --git a/docs/src/examples/nonlinear_hyperbolic.md b/docs/src/examples/nonlinear_hyperbolic.md
@@ -37,7 +37,8 @@ using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, Roots,
       LineSearches
 using SpecialFunctions
 using Plots
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u(..), w(..)
@@ -105,7 +106,7 @@ res = Optimization.solve(prob, BFGS(linesearch = BackTracking()); maxiters = 200
 phi = discretization.phi
 
 # Analysis
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 depvars = [:u, :w]
 minimizers_ = [res.u.depvar[depvars[i]] for i in 1:length(chain)]
 
diff --git a/docs/src/examples/wave.md b/docs/src/examples/wave.md
@@ -18,6 +18,7 @@ Further, the solution of this equation with the given boundary conditions is pre
 ```@example wave
 using NeuralPDE, Lux, Optimization, OptimizationOptimJL
 using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u(..)
@@ -64,7 +65,7 @@ We can plot the predicted solution of the PDE and compare it with the analytical
 ```@example wave
 using Plots
 
-ts, xs = [infimum(d.domain):dx:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):dx:rightendpoint(d.domain) for d in domains]
 function analytic_sol_func(t, x)
     sum([(8 / (k^3 * pi^3)) * sin(k * pi * x) * cos(C * k * pi * t) for k in 1:2:50000])
 end
@@ -99,7 +100,8 @@ with grid discretization `dx = 0.05` and physics-informed neural networks. Here,
 ```@example wave2
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL
 using Plots, Printf
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u(..) Dxu(..) Dtu(..) O1(..) O2(..)
@@ -169,7 +171,7 @@ res = Optimization.solve(prob, BFGS(); maxiters = 2000)
 phi = discretization.phi[1]
 
 # Analysis
-ts, xs = [infimum(d.domain):0.05:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.05:rightendpoint(d.domain) for d in domains]
 
 μ_n(k) = (v * sqrt(4 * k^2 * π^2 - b^2 * L^2 * v^2)) / (2 * L)
 function b_n(k)
@@ -200,7 +202,7 @@ end
 gif(anim, "1Dwave_damped_adaptive.gif", fps = 200)
 
 # Surface plot
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 u_predict = reshape(
     [first(phi([t, x], res.u.depvar.u)) for
      t in ts for x in xs], (length(ts), length(xs)))
diff --git a/docs/src/tutorials/constraints.md b/docs/src/tutorials/constraints.md
@@ -23,7 +23,8 @@ with Physics-Informed Neural Networks.
 ```@example fokkerplank
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, LineSearches
 using Integrals, Cubature
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 # the example is taken from this article https://arxiv.org/abs/1910.10503
 @parameters x
 @variables p(..)
@@ -95,7 +96,7 @@ using Plots
 C = 142.88418699042 #fitting param
 analytic_sol_func(x) = C * exp((1 / (2 * _σ^2)) * (2 * α * x^2 - β * x^4))
 
-xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
+xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains][1]
 u_real = [analytic_sol_func(x) for x in xs]
 u_predict = [first(phi(x, res.u)) for x in xs]
 
diff --git a/docs/src/tutorials/derivative_neural_network.md b/docs/src/tutorials/derivative_neural_network.md
@@ -54,7 +54,8 @@ using the second numeric derivative `Dt(Dtu1(t,x))`.
 ```@example derivativenn
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimisers,
       OptimizationOptimJL, LineSearches, Plots
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 Dt = Differential(t)
@@ -127,7 +128,7 @@ And some analysis:
 ```@example derivativenn
 using Plots
 
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 minimizers_ = [res.u.depvar[sym_prob.depvars[i]] for i in 1:length(chain)]
 
 u1_real(t, x) = exp(-t) * sinpi(x)
diff --git a/docs/src/tutorials/dgm.md b/docs/src/tutorials/dgm.md
@@ -54,7 +54,8 @@ u(t, 1) & = 0
 using NeuralPDE
 using ModelingToolkit, Optimization, OptimizationOptimisers
 using Distributions
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using MethodOfLines, OrdinaryDiffEq
 using Plots
 
diff --git a/docs/src/tutorials/gpu.md b/docs/src/tutorials/gpu.md
@@ -46,6 +46,7 @@ using NeuralPDE, Lux, LuxCUDA, Random, ComponentArrays
 using Optimization
 using OptimizationOptimisers
 import DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using Plots
 using Printf
 
@@ -111,7 +112,7 @@ Finally, we inspect the solution:
 
 ```@example gpu
 phi = discretization.phi
-ts, xs, ys = [infimum(d.domain):0.1:supremum(d.domain) for d in domains]
+ts, xs, ys = [leftendpoint(d.domain):0.1:rightendpoint(d.domain) for d in domains]
 u_real = [analytic_sol_func(t, x, y) for t in ts for x in xs for y in ys]
 u_predict = [first(Array(phi([t, x, y], res.u))) for t in ts for x in xs for y in ys]
 
diff --git a/docs/src/tutorials/integro_diff.md b/docs/src/tutorials/integro_diff.md
@@ -45,7 +45,8 @@ u(0) = 0
 
 ```@example integro
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, DomainSets
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using Plots
 
 @parameters t
@@ -72,7 +73,7 @@ res = Optimization.solve(prob, BFGS(); maxiters = 100)
 Plotting the final solution and analytical solution
 
 ```@example integro
-ts = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
+ts = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains][1]
 phi = discretization.phi
 u_predict = [first(phi([t], res.u)) for t in ts]
 
diff --git a/docs/src/tutorials/low_level.md b/docs/src/tutorials/low_level.md
@@ -14,7 +14,8 @@ with Physics-Informed Neural Networks. Here is an example of using the low-level
 
 ```@example low_level
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, LineSearches
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u(..)
@@ -73,7 +74,7 @@ And some analysis:
 ```@example low_level
 using Plots
 
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 u_predict_contourf = reshape([first(phi([t, x], res.u)) for t in ts for x in xs],
     length(xs), length(ts))
 plot(ts, xs, u_predict_contourf, linetype = :contourf, title = "predict")
diff --git a/docs/src/tutorials/low_level_2.md b/docs/src/tutorials/low_level_2.md
@@ -29,7 +29,8 @@ With Bayesian Physics-Informed Neural Networks, here is an example of using `Bay
 ```@example low_level_2
 using NeuralPDE, Lux, ModelingToolkit, LinearAlgebra, AdvancedHMC
 import ModelingToolkit: Distributions
-import DomainSets: Interval, infimum, supremum
+import DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using Plots, MonteCarloMeasurements
 
 @parameters x, t, α
@@ -131,7 +132,7 @@ And some analysis:
 ```@example low_level_2
 phi = discretization.phi[1]
 xs,
-ts = [infimum(d.domain):dx:supremum(d.domain)
+ts = [leftendpoint(d.domain):dx:rightendpoint(d.domain)
       for (d, dx) in zip(domains, [dx / 10, dt])]
 u_predict = [[first(pmean(phi([x, t], sol1.estimated_nn_params[1]))) for x in xs]
              for t in ts]
diff --git a/docs/src/tutorials/neural_adapter.md b/docs/src/tutorials/neural_adapter.md
@@ -15,7 +15,8 @@ Using the example of 2D Poisson equation, it is shown how, using the method neur
 
 ```@example neural_adapter
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimisers
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using Random, ComponentArrays
 
 @parameters x y
diff --git a/docs/src/tutorials/param_estim.md b/docs/src/tutorials/param_estim.md
@@ -17,7 +17,8 @@ We start by defining the problem,
 ```@example param_estim
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, OrdinaryDiffEq,
       Plots, LineSearches
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 @parameters t, σ_, β, ρ
 @variables x(..), y(..), z(..)
 Dt = Differential(t)
@@ -58,7 +59,7 @@ u0 = [1.0; 0.0; 0.0]
 tspan = (0.0, 1.0)
 prob = ODEProblem(lorenz!, u0, tspan)
 sol = solve(prob, Tsit5(), dt = 0.1)
-ts = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
+ts = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains][1]
 function getData(sol)
     data = []
     us = hcat(sol(ts).u...)
@@ -111,7 +112,7 @@ And then finally some analysis by plotting.
 
 ```@example param_estim
 minimizers = [res.u.depvar[depvars[i]] for i in 1:3]
-ts = [infimum(d.domain):(0.001):supremum(d.domain) for d in domains][1]
+ts = [leftendpoint(d.domain):(0.001):rightendpoint(d.domain) for d in domains][1]
 u_predict = [[discretization.phi[i]([t], minimizers[i])[1] for t in ts] for i in 1:3]
 plot(sol)
 plot!(ts, u_predict, label = ["x(t)" "y(t)" "z(t)"])
diff --git a/docs/src/tutorials/pdesystem.md b/docs/src/tutorials/pdesystem.md
@@ -30,6 +30,7 @@ Using physics-informed neural networks.
 ```@example poisson
 using NeuralPDE, Lux, Optimization, OptimizationOptimJL, LineSearches, Plots
 using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters x y
 @variables u(..)
@@ -70,7 +71,7 @@ res = solve(prob, opt, maxiters = 1000)
 phi = discretization.phi
 
 dx = 0.05
-xs, ys = [infimum(d.domain):(dx / 10):supremum(d.domain) for d in domains]
+xs, ys = [leftendpoint(d.domain):(dx / 10):rightendpoint(d.domain) for d in domains]
 analytic_sol_func(x, y) = (sin(pi * x) * sin(pi * y)) / (2pi^2)
 
 u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
@@ -92,6 +93,7 @@ The ModelingToolkit PDE interface for this example looks like this:
 ```@example poisson
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL
 using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 using Plots
 
 @parameters x y
@@ -155,7 +157,7 @@ We can plot the predicted solution of the PDE and compare it with the analytical
 
 ```@example poisson
 dx = 0.05
-xs, ys = [infimum(d.domain):(dx / 10):supremum(d.domain) for d in domains]
+xs, ys = [leftendpoint(d.domain):(dx / 10):rightendpoint(d.domain) for d in domains]
 analytic_sol_func(x, y) = (sin(pi * x) * sin(pi * y)) / (2pi^2)
 
 u_predict = reshape([first(phi([x, y], res.u)) for x in xs for y in ys],
diff --git a/docs/src/tutorials/systems.md b/docs/src/tutorials/systems.md
@@ -37,7 +37,8 @@ with physics-informed neural networks.
 ```@example system
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL, LineSearches,
       OptimizationOptimisers
-using DomainSets: Interval, infimum, supremum
+using DomainSets: Interval
+using IntervalSets: leftendpoint, rightendpoint
 
 @parameters t, x
 @variables u1(..), u2(..), u3(..)
@@ -124,7 +125,7 @@ Now let's perform some analysis for both the `symbolic_discretize` and `discreti
 using Plots
 
 phi = discretization.phi
-ts, xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
+ts, xs = [leftendpoint(d.domain):0.01:rightendpoint(d.domain) for d in domains]
 
 minimizers_ = [res.u.depvar[sym_prob.depvars[i]] for i in 1:3]
 
