Merge branch 'SciML:master' into non_diagonal_noise

.buildkite/pipeline.yml

@@ -75,4 +75,4 @@ steps:
 
 env:
  JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
- SECRET_DOCUMENTER_KEY: "B9R7saQARpCbndRchBC7rJziRQiKEgbi59ob+n+l73+Vsmfw6nKMew83nedo3vsxJsLWfT3fMHfF47JBWNU7FVpulHil/rsiiEP8gSQ8ZyB0RS3G0fMMTEqL7sngRhokxUqiD3/wHtTu1Ep/xIpNk6P0XYXaUBQWriTAYG+c+I17fz0XLMgsOu9Y1zhL0kKzyjf4OVWnqS4igCc/Z2uf3Odpw19M4QbwQ5Bds2fLJ+/tFLpqdIoTbfAut9Z/8yProf2TmEhmIICDixXJ7b1TP//7/mVTEG4xYjjqWASkmgAgarH8uxwfbxIcM7+ypbLckc4b3Yq7c8HtrM8UhhKVnw==;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" # For authentication with SSH deploy key
+ SECRET_DOCUMENTER_KEY: "gY1VHLeyPtvVks8hT0Aq9+OZs7pEyN0X1x+yJg4rbVJ27Ks5DngTIu9WCKgENo1yJOMl1a9Sik+g2DSmocqIhkxnkx0R6KvGm/4eDKmS2fFbr3vNaSK2Ja5AXcB2wShpsc5I0tV1zc6Jvnqh4GB7dF1XqEdAg6Ga8LTINtAI64eItreG1AyevCLvMtbzUlTblQtFrj12n0bgdZbOtnS1/2devwk65V69jKiG+aupbN2jvIF3/Z1XA+hBmJKkWaEXf0Gj2UVxLMinMExxlDki79uJi5gQyit4t5E2DWd/+17Zr7B0hu7+Q70NgZqdHwQlPRQ+E7H+OZsxTZIVkjel5w==;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"

.github/workflows/CompatHelper.yml

@@ -23,4 +23,4 @@ jobs:
  - name: CompatHelper.main()
  env:
  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- run: julia -e 'using CompatHelper; CompatHelper.main()'
+ run: julia -e 'using CompatHelper; CompatHelper.main(;subdirs=["", "docs", "test/gpu"])'

Project.toml

@@ -1,7 +1,7 @@
 name = "SciMLSensitivity"
 uuid = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 authors = ["Christopher Rackauckas <[email protected]>", "Yingbo Ma <[email protected]>"]
-version = "7.11.0"
+version = "7.11.1"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

docs/Project.toml

@@ -1,13 +1,13 @@
 [deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503"
+DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
@@ -20,7 +20,7 @@ NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationFlux = "253f991c-a7b2-45f8-8852-8b9a9df78a86"
-OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
+OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 OptimizationPolyalgorithms = "500b13db-7e66-49ce-bda4-eed966be6282"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
@@ -38,4 +38,37 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
+CUDA = "3.12"
+Calculus = "0.5"
+ComponentArrays = "0.13"
+DataInterpolations = "3.10"
+DiffEqBase = "6.106"
+DiffEqCallbacks = "2.24"
+DiffEqFlux = "1.52"
+DiffEqNoiseProcess = "5.14"
+DifferentialEquations = "7.6"
 Documenter = "0.27"
+Flux = "0.13"
+ForwardDiff = "0.10"
+GraphNeuralNetworks = "0.5, 0.6"
+IterTools = "1.4"
+Lux = "0.4"
+MLDatasets = "0.7"
+NNlib = "0.8"
+Optimisers = "0.2"
+Optimization = "3.9"
+OptimizationFlux = "0.1"
+OptimizationNLopt = "0.1"
+OptimizationOptimisers = "0.1"
+OptimizationPolyalgorithms = "0.1"
+OrdinaryDiffEq = "6.31"
+Plots = "1.36"
+QuadGK = "2.6"
+RecursiveArrayTools = "2.32"
+ReverseDiff = "1.14"
+SciMLSensitivity = "7.11"
+SimpleChains = "0.4"
+StaticArrays = "1.5"
+StochasticDiffEq = "6.56"
+Tracker = "0.2"
+Zygote = "0.6"

docs/pages.jl

@@ -1,31 +1,31 @@
 pages = ["index.md",
- "Tutorials" => Any["Differentiating Ordinary Differential Equations (ODE) Tutorials" => Any["ad_examples/differentiating_ode.md",
-  "ad_examples/direct_sensitivity.md",
-  "ad_examples/adjoint_continuous_functional.md",
-  "ad_examples/chaotic_ode.md"],
- "Fitting Ordinary Differential Equation (ODE) Tutorials" => Any["ode_fitting/optimization_ode.md",
-  "ode_fitting/exogenous_input.md",
-  "ode_fitting/data_parallel.md",
-  "ode_fitting/prediction_error_method.md",
-  "ode_fitting/second_order_adjoints.md",
-  "ode_fitting/second_order_neural.md"],
- "Training Techniques and Tips" => Any["training_tips/local_minima.md",
- "training_tips/divergence.md",
- "training_tips/multiple_nn.md"],
-  "Neural Ordinary Differential Equation (Neural ODE) Tutorials" => Any["neural_ode/neural_ode_flux.md",
-  "neural_ode/simplechains.md",
-  "neural_ode/neural_gde.md",
-  "neural_ode/minibatch.md"],
-  "Stochastic Differential Equation (SDE) Tutorials" => Any["sde_fitting/optimization_sde.md"],
- "Delay Differential Equation (DDE) Tutorials" => Any["dde_fitting/delay_diffeq.md"],
-  "Differential-Algebraic Equation (DAE) Tutorials" => Any["dae_fitting/physical_constraints.md"],
-  "Partial Differential Equation (PDE) Tutorials" => Any["pde_fitting/pde_constrained.md"],
-  "Hybrid and Jump Equation Tutorials" => Any["hybrid_jump_fitting/hybrid_diffeq.md",
-  "hybrid_jump_fitting/bouncing_ball.md"],
- "Bayesian Estimation Tutorials" => Any["bayesian/turing_bayesian.md"],
-  "Optimal and Model Predictive Control Tutorials" => Any["optimal_control/optimal_control.md",
-  "optimal_control/feedback_control.md",
-  "optimal_control/SDE_control.md"]],
+ "getting_started.md",
+ "Tutorials" => Any["tutorials/parameter_estimation_ode.md",
+ "tutorials/direct_sensitivity.md",
+ "tutorials/adjoint_continuous_functional.md",
+ "tutorials/data_parallel.md",
+ "tutorials/chaotic_ode.md",
+ "Training Techniques and Tips" => Any["tutorials/training_tips/local_minima.md",
+ "tutorials/training_tips/divergence.md",
+ "tutorials/training_tips/multiple_nn.md"]],
+ "Examples" => Any["Ordinary Differential Equations (ODEs)" => Any["examples/ode/exogenous_input.md",
+  "examples/ode/prediction_error_method.md",
+  "examples/ode/second_order_adjoints.md",
+  "examples/ode/second_order_neural.md"],
+ "Neural Ordinary Differential Equations (Neural ODE)" => Any["examples/neural_ode/neural_ode_flux.md",
+ "examples/neural_ode/simplechains.md",
+ "examples/neural_ode/neural_gde.md",
+ "examples/neural_ode/minibatch.md"],
+ "Stochastic Differential Equations (SDEs)" => Any["examples/sde/optimization_sde.md",
+  "examples/sde/SDE_control.md"],
+ "Delay Differential Equations (DDEs)" => Any["examples/dde/delay_diffeq.md"],
+ "Differential-Algebraic Equations (DAEs)" => Any["examples/dae/physical_constraints.md"],
+ "Partial Differential Equations (PDEs)" => Any["examples/pde/pde_constrained.md"],
+ "Hybrid and Jump Equations" => Any["examples/hybrid_jump/hybrid_diffeq.md",
+  "examples/hybrid_jump/bouncing_ball.md"],
+ "Bayesian Estimation" => Any["examples/bayesian/turing_bayesian.md"],
+ "Optimal and Model Predictive Control" => Any["examples/optimal_control/optimal_control.md",
+ "examples/optimal_control/feedback_control.md"]],
  "Manual and APIs" => Any["manual/differential_equation_sensitivities.md",
  "manual/nonlinear_solve_sensitivities.md",
  "manual/direct_forward_sensitivity.md",

docs/src/dae_fitting/physical_constraints.md → docs/src/examples/dae/physical_constraints.md

@@ -9,7 +9,7 @@ zeros, then we have a constraint defined by the right hand side. Using
 terms must add to one. An example of this is as follows:
 
 ```@example dae
-using Lux, DiffEqFlux, Optimization, OptimizationOptimJL, DifferentialEquations, Plots
+using Lux, DiffEqFlux, Optimization, OptimizationNLopt, DifferentialEquations, Plots
 
 using Random
 rng = Random.default_rng()
@@ -64,7 +64,7 @@ l1 = first(loss_stiff_ndae(Lux.ComponentArray(pinit)))
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x,p) -> loss_stiff_ndae(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, Lux.ComponentArray(pinit))
-result_stiff = Optimization.solve(optprob, BFGS(), maxiters=100)
+result_stiff = Optimization.solve(optprob, NLopt.LD_LBFGS(), maxiters=100)
 ```
 
 
@@ -73,7 +73,7 @@ result_stiff = Optimization.solve(optprob, BFGS(), maxiters=100)
 ### Load Packages
 
 ```@example dae2
-using Lux, DiffEqFlux, Optimization, OptimizationOptimJL, DifferentialEquations, Plots
+using Lux, DiffEqFlux, Optimization, OptimizationNLopt, DifferentialEquations, Plots
 
 using Random
 rng = Random.default_rng()
@@ -209,5 +209,5 @@ Finally, training with `Optimization.solve` by passing: *loss function*, *model
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x,p) -> loss_stiff_ndae(x), adtype)
 optprob = Optimization.OptimizationProblem(optf, Lux.ComponentArray(pinit))
-result_stiff = Optimization.solve(optprob, BFGS(), maxiters=100)
+result_stiff = Optimization.solve(optprob, NLopt.LD_LBFGS(), maxiters=100)
 ```

docs/src/hybrid_jump_fitting/hybrid_diffeq.md → docs/src/examples/hybrid_jump/hybrid_diffeq.md

@@ -1,7 +1,8 @@
 # Training Neural Networks in Hybrid Differential Equations
 
 Hybrid differential equations are differential equations with implicit or
-explicit discontinuities as specified by [callbacks](https://docs.sciml.ai/DiffEqDocs/stable/features/callback_functions/).
+explicit discontinuities as specified by 
+[callbacks](https://docs.sciml.ai/DiffEqDocs/stable/features/callback_functions/).
 In the following example, explicit dosing times are given for a pharmacometric
 model and the universal differential equation is trained to uncover the missing
 dynamical equations.