Update ReducedComplexityModeling requirement from 0.1.1 to 0.1.1, 0.3

[dependabot]

Updates the requirements on ReducedComplexityModeling to permit the latest version.

Release notes

Sourced from ReducedComplexityModeling's releases.

v0.3.0

ReducedComplexityModeling v0.3.0

Diff since v0.2.0

Breaking Changes

• [compat] AbstractNeuralNetworks is "0.8" alone, and julia is "1.11". AbstractNeuralNetworks 0.8 requires Julia 1.11, so a Julia 1.10 environment no longer resolves this package; 0.2.0 is the last release that loads there. The bound is what makes the package installable beside GeometricOptimizers 0.7 or 0.8, both of which require AbstractNeuralNetworks 0.8, and therefore usable by SparseIdentification.
• accuracy(::Chain, ps, ::DataLoader) types ps as NetworkParameters, so a caller that passes anything else gets a MethodError at the call rather than a failure deeper in. NeuralNetworkParameters joins [deps], because AbstractNeuralNetworks 0.7 moved the parameters type into that package and 0.8 imports it without exporting it. That release also removed QPT and QPTOAT, which this package used only as dispatch annotations in the data-loader fork, so it now defines both itself, copied from AbstractNeuralNetworks 0.6.4, and imports only Chain and NeuralNetwork. No data format, result or public name changes.

Merged pull requests:

• Admit AbstractNeuralNetworks 0.8 (#56) (@​michakraus)

Changelog

Sourced from ReducedComplexityModeling's changelog.

[0.3.0] — 2026-10-12

Breaking Changes

• [compat] AbstractNeuralNetworks is "0.8" alone, and julia is "1.11". AbstractNeuralNetworks 0.8 requires Julia 1.11, so a Julia 1.10 environment no longer resolves this package; 0.2.0 is the last release that loads there. The bound is what makes the package installable beside GeometricOptimizers 0.7 or 0.8, both of which require AbstractNeuralNetworks 0.8, and therefore usable by SparseIdentification.
• accuracy(::Chain, ps, ::DataLoader) types ps as NetworkParameters, so a caller that passes anything else gets a MethodError at the call rather than a failure deeper in. NeuralNetworkParameters joins [deps], because AbstractNeuralNetworks 0.7 moved the parameters type into that package and 0.8 imports it without exporting it. That release also removed QPT and QPTOAT, which this package used only as dispatch annotations in the data-loader fork, so it now defines both itself, copied from AbstractNeuralNetworks 0.6.4, and imports only Chain and NeuralNetwork. No data format, result or public name changes.

[0.2.0] — 2026-10-11

New Features

• Two new parameter samplers for use with ParameterSpace. RandomParameterSampler(n, rng = Random.default_rng()) draws uniform samples from each parameter's [minimum, maximum] interval; with a seeded rng, the sequence is reproducible. QuasiRandomParameterSampler(n) generates the first n points of a Halton sequence (the j-th parameter uses the j-th prime as its base, starting from index 1), scaled to the parameter box. Both subtype ParameterSampler, ignore any stored samples, and work with sample and the ParameterSpace(sampler, params...) constructor.

• HDF5 groups created by save_parameters, h5save(::Parameter), and h5save(::ParameterSpace) now track link creation order, so keys are preserved in the order written. Previously, keys were reordered alphabetically when read back. A file written before this change reads with keys in alphabetical order. A group that exists before the write and does not track creation order, such as the file root in save_parameters(h5, params; path = "/"), keeps the alphabetical order. The HDF5 compat bound rises to 0.16.11, 0.17, because 0.16.11 is the first HDF5.jl release where keys(group) returns a group's creation order.

• Training data and training problems are first class. TrainingData{SpaceType, DataType, SystemType}(time, data[, problem]) tags data with three orthogonal axes: the space (ObservableSpace or IntrinsicSpace), what was recorded (DataType, one of GeometricBase's ObservableData, StateData, VectorFieldData and TangentVectorData) and the structure of the system (SystemType, one of GeometricBase's concrete systems, such as CanonicalHamiltonianSystem). The last two dispatch state_symbols, which returns (:q, :p) for state data on a canonical Hamiltonian system and (:z,) on a noncanonical one, so a method that expects one kind of data no longer accepts another in silence. A shape mismatch between time and data throws a DimensionMismatch. The ensemble constructor, TrainingData{SpaceType, DataType, SystemType}(solution::EnsembleSolution), builds data from the variables that state_symbols names, and time from the stored times. TrainingProblem(data, residual, parameters) holds the data, a residual residual(data, θ::NamedTuple) and a ParameterSpace, and train(problem, method::Optim.FirstOrderOptimizer) fits the parameters by minimising the squared

... (truncated)

Commits

• e111300 Release 0.3.0
• e18d444 Admit AbstractNeuralNetworks 0.8 (#56)
• 3b0554e Release 0.2.0
• a93bd88 Training data, training problems and train (#53)
• 01416d0 Sync the shared git hooks
• a5373a7 Move the doctests into their own test group (#52)
• 61d38ef CI: coverage from Julia 1 on ubuntu, save the cache only from a job that succ...
• 9cdb70f Sync the shared git hooks
• 6a806ff Sync the shared hooks and workflows from the harness
• 91dc225 Update githooks: pre-commit and test-layout.jl
• Additional commits viewable in compare view

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 91.69%. Comparing base (a8fa5c5) to head (3d3e486).

Additional details and impacted files

@@           Coverage Diff           @@
##             main      #58   +/-   ##
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  Coverage   91.69%   91.69%           
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  Files          11       11           
  Lines         337      337           
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  Hits          309      309           
  Misses         28       28           

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