docs: use BackTracking in BFGS for linear parabolic example

SciML · Dec 12, 2023 · c2d7a55 · c2d7a55
1 parent 80239f9
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diff --git a/docs/Project.toml b/docs/Project.toml
@@ -6,6 +6,7 @@ DomainSets = "5b8099bc-c8ec-5219-889f-1d9e522a28bf"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Integrals = "de52edbc-65ea-441a-8357-d3a637375a31"
 IntegralsCubature = "c31f79ba-6e32-46d4-a52f-182a8ac42a54"
+LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NeuralPDE = "315f7962-48a3-4962-8226-d0f33b1235f0"

diff --git a/docs/src/examples/linear_parabolic.md b/docs/src/examples/linear_parabolic.md
@@ -27,6 +27,7 @@ with a physics-informed neural network.
 using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimJL
 using Plots
 import ModelingToolkit: Interval, infimum, supremum
+using LineSearches
 
 @parameters t, x
 @variables u(..), w(..)
@@ -92,7 +93,7 @@ callback = function (p, l)
     return false
 end
 
-res = Optimization.solve(prob, BFGS(); callback = callback, maxiters = 5000)
+res = Optimization.solve(prob, BFGS(linesearch = BackTracking()); callback = callback, maxiters = 5000)
 
 phi = discretization.phi