From 2b641a9c2f4b30a9cced6a4cef6e034bc9936037 Mon Sep 17 00:00:00 2001 From: ChrisRackauckas Date: Mon, 22 Sep 2025 02:15:31 -0400 Subject: [PATCH] Fix module reference errors in missing physics example MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The example had incorrect module references that prevented it from running: - Line 273: `ODE.solve` should be `OPT.solve` for optimization - Line 495: `Optimization.OptimizationFunction` should be `OPT.OptimizationFunction` - Line 496: `Optimization.OptimizationProblem` should be `OPT.OptimizationProblem` - Line 497: `Optimization.OPT.solve` should be `OPT.solve` - Line 497: `Optim.LBFGS()` should be `OptimizationOptimJL.LBFGS()` These were typos introduced during the module aliasing refactor. The example now runs correctly with just these minimal fixes. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- docs/src/showcase/missing_physics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/src/showcase/missing_physics.md b/docs/src/showcase/missing_physics.md index ee6f8fc373..e6488518af 100644 --- a/docs/src/showcase/missing_physics.md +++ b/docs/src/showcase/missing_physics.md @@ -270,7 +270,7 @@ Thus we first solve the optimization problem with ADAM. Choosing a learning rate (tuned to be as high as possible that doesn't tend to make the loss shoot up), we see: ```@example ude -res1 = ODE.solve( +res1 = OPT.solve( optprob, OptimizationOptimisers.Adam(), callback = callback, maxiters = 5000) println("Training loss after $(length(losses)) iterations: $(losses[end])") ``` @@ -488,9 +488,9 @@ function parameter_loss(p) sum(abs2, Ŷ .- Y) end -optf = Optimization.OptimizationFunction((x, p) -> parameter_loss(x), adtype) -optprob = Optimization.OptimizationProblem(optf, DataDrivenDiffEq.get_parameter_values(nn_eqs)) -parameter_res = Optimization.OPT.solve(optprob, Optim.LBFGS(), maxiters = 1000) +optf = OPT.OptimizationFunction((x, p) -> parameter_loss(x), adtype) +optprob = OPT.OptimizationProblem(optf, DataDrivenDiffEq.get_parameter_values(nn_eqs)) +parameter_res = OPT.solve(optprob, OptimizationOptimJL.LBFGS(), maxiters = 1000) ``` ## Simulation