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Run differentiable AAA calculations, locate poles, and work with vector or tensor functions
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Developer toolsPython · MATLABGitHub @jan-david-fischbachOnlineOwner unconfirmed
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diffaaable is a JAX differentiable version of the AAA algorithm. The derivatives are implemented as custom Jacobian Vector products in accordance to [^1].
A detailed derivation of the used matrix expressions is provided in the appendix of [^2].
Under the hood diffaaable uses the AAA implementation of baryrat.
Additionaly the following application specific extensions to the AAA algorithm are included:Read the full README ↗diffaaable is a JAX differentiable version of the AAA algorithm. The derivatives are implemented as custom Jacobian-vector products (JVPs) following 1.
A detailed derivation of the used matrix expressions is provided in the appendix of 2.
Under the hood diffaaable uses the AAA implementation of baryrat.
Additionally the following application specific extensions to the AAA algorithm are included:
to install diffaaable run
pip install diffaaable
Please refer to the quickstart tutorial
Feel free to open issues and/or PRs.
User facing changes are recorded with towncrier:
add a one-line fragment to .changelog.d/, named <PR number>.<type>.md
(or +<name>.<type>.md without a PR), where <type> is one of added,
changed, performance, fixed, deprecated, removed or security, e.g.
towncrier create -c "Faster set_aaa." +faster-set-aaa.performance.md.
When using this software package for scientific work please cite the associated publication 2.
When using the tensor-valued AAA (tensor_aaa), e.g. for pole-expansions of T-matrices, please also cite 4.
+++
"A framework to compute resonances arising from multiple scattering", https://doi.org/10.1002/adts.202400989 ↩ ↩2
"Pole-Expansion of the T-Matrix Based on a Matrix-Valued AAA-Algorithm", ACS Photonics 13, 5291 (2026), https://doi.org/10.1021/acsphotonics.6c00436 ↩ ↩2