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docs: use BackTracking in BFGS for linear parabolic example
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sathvikbhagavan committed Dec 11, 2023
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1 change: 1 addition & 0 deletions docs/Project.toml
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Expand Up @@ -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"
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3 changes: 2 additions & 1 deletion docs/src/examples/linear_parabolic.md
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Expand Up @@ -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(..)
Expand Down Expand Up @@ -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
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