What’s New

What’s New#

Attention

opinf is a research code that is still in rapid development. New versions may introduce substantial new features or API adjustments.

Version 0.5.16#

Backend improvements to the regularization selection procedure.

  • ROM.fit() calls ROM.model.fit() instead of ROM.model._fit_solver(), which works better for inheritance.

  • BayesianROM.fit_regselect_*() catches errors and suppresses warnings from the least-squares solver.

  • Fixed a bug for interpolatory parametric models where fit_regselect_*() would only update the regularizer for the overall model.solver, not for each of the individual solvers for the models to be interpolated.

  • Fixed a bug related to test cases being processed before the basis was initialized.

  • Removed unnecessary abstract methods from models.mono._base._OpInfModel.

  • Small updates to the literature page.

Version 0.5.15#

  • Improvement to fit_regselect_*() so that the regularization does not have to be initialized before fitting the model. This fixes a longstanding chicken/egg problem and makes using fit_regselect_*() much less cumbersome.

  • Ensured error computation correctly aligns training and predicted states in fit_regselect_continuous(). Previously, this could have been misaligned when using a time derivative estimator that truncates states.

  • Time derivative estimators in opinf.ddt now have a mask() method that map states to the estimation grid.

  • Added regression tests based on the tutorial notebooks.

  • Added pytest and pytest-cov to the dependencies for developers.

Version 0.5.14#

  • Catch any errors in fit_regselect*() that occur when the model uses refit().

  • Tikhonov-type least-squares solvers do not require the regularizer in the constructor but will raise an AttributeError in solve() (and other methods) if the regularizer is not set.

  • PODBasis.fit(Q) raises a warning when using the "method-of-snapshots"/"eigh" strategy if \(n < k\) for \(\mathbf{Q}\in\mathbb{R}^{n \times k}.\) In this case, calculating the \(n \times k\) SVD is likely more efficient than the \(k \times k\) eigenvalue problem.

  • Added Python 3.13 to list of tests.

Version 0.5.13#

Bayesian operator inference:

  • New roms.BayesianROM class.

  • New supporting class roms.OperatorPosterior.

  • Updates to relevant least-squares solvers.

These changes implement the framework proposed in [GMW22].

Version 0.5.12#

  • New operators.QuarticOperator, plus unit tests.

  • Reorganized some unit tests for models and operators to have OOP structure.

  • Bugfix: fit_regselect_continuous() now returns self

Version 0.5.11#

  • New scaling option for pre.ShiftScaleTransformer so that training snapshots have at maximum norm 1. Contributed by @nicolearetz.

  • Small clarifications to pre.ShiftScaleTransformer and updates to the pre documentation.

Version 0.5.10#

New POD basis solver option basis.PODBasis(solver="method-of-snapshots") (or solver="eigh"), which solves a symmetric eigenvalue problem instead of computing a (weighted) SVD. This method is more efficient than the SVD for snapshot matrices \(\mathbf{Q}\in\mathbb{R}^{n\times k}\) where \(n \gg k\) and is significantly more efficient than the SVD when a non-diagonal weight matrix is provided. Contributed by @nicolearetz.

Version 0.5.9#

Automatic regularization selection:

  • New methods fit_regselect_continuous() and fit_regselect_discrete() in the ROM and ParametricROM classes.

  • utils.gridsearch() implements grid search followed up by derivative-free log-scale optimization.

  • Class method TikhonovSolver.get_operator_regularizer() to construct diagonal Tikhonov regularizers where each operator is regularized by a different scalar.

New transformers for custom shifting / scaling:

  • pre.ShiftTransformer

  • pre.ScaleTransformer

  • pre.NullTransformer

  • pre.TransformerPipeline

Small improvements:

  • Executing individual test files now runs the tests contained within.

  • utils.TimedBlock has a rebuffer attribute that, when set to True, prevents printing until the end of the block.

  • Improved test coverage, fixed some documentation typos, etc.

Version 0.5.8#

Support for affine-parametric problems:

  • Affine-parametric operator classes AffineConstantOperator, AffineLinearOperator, etc.

  • Parametric model classes ParametricContinuousModel, ParametricDiscreteModel.

  • ParametricROM class.

  • Updates to operator / model documentation.

Renamed interpolatory operators / model classes from Interpolated<Name> to Interp<Name>. Old names are deprecated but not yet removed.

Miscellaneous:

  • Reorganized and expanded tutorials.

  • Added and documented opinf.utils.TimedBlock context manager for quick timing of code blocks.

  • Updated structure for some unit tests.

  • Refactored interpolatory operators.

  • Standardized string representations, added [Parametric]ROM.__str__().

  • Removed some public functions from operators, regrouped in operators._utils.

  • Removed some public functions from models, regrouped in models._utils.

Version 0.5.7#

Updates to opinf.lstsq:

  • New TruncatedSVDSolver class.

  • predict() has been renamed solve() for opinf.lstsq solver classes to not clash with predict() from opinf.roms / opinf.models classes.

  • solve() always returns a two-dimensional array, even if \(r = 1\).

Various small improvements to tests and documentation.

Version 0.5.6#

Added public templates to opinf.operators:

  • OperatorTemplate for general nonparametric operators.

  • OpInfOperator for nonparametric operators that can be learned through Operator Inference (operator matrix times data vector structure).

  • ParametricOperatorTemplate for general parametric operators.

  • ParametricOpInfOperator for parametric operators that can be learned through Operator Inference.

Also added a new opinf.ddt.InterpolationDerivativeEstimator class and made various small changes for compatibility with NumPy 2.0.0.

Version 0.5.5#

Changes to the opinf.lstsq API and improvements to the documentation.

  • opinf.model classes now receive solvers in the constructor, not in fit(). This change will be useful for future models that require specific solvers. Updated ROM class and tutorials accordingly.

  • New SolverTemplate class and inheritance guide for creating new solvers.

  • Renamed attributes to match OpInf terminology.

    • A --> data_matrix, called \(\D\) in the docs.

    • B --> lhs_matrix, called \(\Z\) in the docs. Warning: fit() receives \(\Z\), not \(\Z\trp\)!

    • \(X\) is replaced with \(\Ohat\). Warning: predict() returns \(\Ohat\), not \(\Ohat\trp\)!

  • Renamed two Tikhonov solver classes:

    • L2SolverDecoupled --> L2DecoupledSolver

    • TikhonovSolverDecoupled --> TikhonovDecoupledSolver

  • Tikhonov solvers no longer have a default regularization value of zero.

Before:

>>> model = opinf.models.ContinuousModel("A")
>>> solver = opinf.lstsq.L2Solver(regularizer=1e-2)
>>> model.fit(states, ddts, inputs, solver=solver)

After:

>>> solver = opinf.lstsq.L2Solver(regularizer=1e-2)
>>> model = opinf.models.ContinuousModel("A", solver=solver)
>>> model.fit(states, ddts, inputs)

Version 0.5.4#

New opinf.roms submodule containing an opinf.roms.ROM class, also available in the main namespace as opinf.ROM. This class wraps a lifter, transformer, basis, time derivative estimator, model, and least-squares solver together for convenience. Rewrote the first tutorial to use opinf.ROM.

Version 0.5.3#

Expanded the ddt submodule (but no API changes to existing functions).

  • New backward, central, and forward difference schemes up to sixth order.

  • New DerivativeEstimatorTemplate class for implementing custom derivative estimation strategies.

  • New UniformFiniteDifference and NonuniformFiniteDifference convenience classes for working with ROM classes in the future.

  • Better documentation of the module.

The documentation was also updated to JupyterBook 1.0.0, a significant improvement to the look and feel.

Tests are now managed with tox, the contributor instructions were updated.

Version 0.5.2#

Significant updates to the pre and basis submodules.

Updates to pre:

  • New TransformerTemplate class for defining custom transformers.

  • Renamed SnapshotTransformer to ShiftScaleTransformer.

  • Removed SnapshotTransformerMulti.

  • New TransformerMulti class for joining multiple transformations.

  • Renamed some attributes of the transformer classes: n -> full_state_dimension, ni -> variable_size, etc.

Updates to basis:

  • New BasisTemplate class for defining custom bases.

  • Standardized arguments of fit() to always be the snapshots. Hyperparameters must come in the constructor.

  • LinearBasis now accepts an optional weight matrix.

  • LinearBasis raises a warning if the basis entries are not orthogonal (w.r.t. the weights).

  • Updated PODBasis dimensionality properties. Use set_dimension() to update the reduced state dimension on the fly.

  • Removed PODBasisMulti.

  • New BasisMulti multi class for joining multiple bases.

  • Renamed some attributes of the basis classes (matching transformer syntax): n -> full_state_dimension, r -> reduced_state_dimension, etc.

Added a new Literature page to the documentation listing Operator Inference publications.

Version 0.5.1#

New lift module that defines a template class for implementing lifting transformations.

Version 0.5.0#

  • Overhauled the operators module so that each operator class is responsible for its portion of the Operator Inference data matrix.

    • New StateInputOperator for state-input bilinear interactions, \(\Nhat[\u\otimes\qhat]\).

    • Operator classes now have state_dimension and input_dimension properties.

    • Operator classes must implement datablock() and operator_dimension() methods to facilitate operator inference.

  • Renamed roms to models and updated class names:

    • ContinuousOpInfROM to ContinuousModel

    • DiscreteOpInfROM to DiscreteModel

    • SteadyOpInfROM to SteadyModel

    • Same for interpolated models.

  • Model classes now take a list of operators in the constructor. String shortcuts such as "cAH" are still valid, similar to the previous modelform argument. The known_operators argument has been removed from the fit() method.

  • Model classes no longer have a basis attribute.

    • The basis argument has been removed from the fit() method.

    • The compress() and decompress() methods have been removed from model classes.

    • The dimensions n and r have been replaced with state_dimension; m is now input_dimension.

  • Moved time derivative estimation tools to the new ddt submodule.

  • Moved Kronecker product utilities to static methods of nonparametric operators.

    • utils.kron2c() is now QuadraticOperator.ckron()

    • utils.kron2c_indices() is now QuadraticOperator.ckron_indices()

    • utils.compress_quadratic() is now QuadraticOperator.compress_entries()

    • utils.expand_quadratic() is now QuadraticOperator.expand_entries()

    • utils.kron3c() is now CubicOperator.ckron()

    • utils.kron3c_indices() is now CubicOperator.ckron_indices()

    • utils.compress_cubic() is now CubicOperator.compress_entries()

    • utils.expand_cubic() is now CubicOperator.expand_entries()

Version 0.4.5#

  • Moved basis classes and dimensionality reduction tools to a new basis submodule.

  • Moved operator classes from core.operators to a new operators submodule.

  • Renamed the core submodule to roms.

  • Moved time derivative estimation tools to the utils module.

Version 0.4.4#

  • Fixed a bug in SnapshotTransformer.load() that treated the centered_ attribute incorrectly.

  • Removed the transformer attribute from basis classes.

  • Renamed encode() to compress() and decode() to decompress().

Version 0.4.2#

  • In the fit() method in ROM classes, replaced the regularizer argument with a solver keyword argument. The user should pass in an instantiation of a least-squares solver class from the lstsq submodule.

  • Hyperparameters for least-squares solver classes in the lstsq submodule are now passed to the constructor; predict() must not take any arguments.

  • Renamed the following least-squares solver classes in the lstsq submodule:

    • SolverL2 -> L2Solver

    • SolverL2Decoupled -> L2SolverDecoupled

    • SolverTikhonov -> TikhonovSolver

    • SolverTikhonovDecoupled -> TikhonovSolverDecoupled

Before:

>>> rom.fit(basis, states, ddts, inputs, regularizer=1e-2)

After:

>>> solver = opinf.lstsq.L2Solver(regularizer=1e-2)
>>> rom.fit(basis, states, ddts, inputs, solver=solver)

# The L2 solver is also the default if a float is given:
>>> rom.fit(basis, states, ddts, inputs, solver=1e-2)

Versions 0.4.0 and 0.4.1#

This version is a migration of the old rom_operator_inference package, version 1.4.1. See this page for the documentation.

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