Abstract
This paper extends interpolatory model reduction to systems with (up to) quadratic-bilinear dynamics and quadratic-bilinear outputs. These systems are referred to as QB-QB systems and arise in a number of applications, including fluid dynamics, optimal control, and uncertainty quantification. In the interpolatory approach, the reduced order models (ROMs) are based on a Petrov-Galerkin projection, and the projection matrices are constructed so that transfer function components of the ROM interpolate the corresponding transfer function components of the original system. To extend the approach to systems with QB outputs, this paper derives system transfer functions and sufficient conditions on the projection matrices that guarantee the aforementioned interpolation properties. Alternatively, if the system has linear dynamics and quadratic outputs, one can introduce auxiliary state variables to transform it into a system with QB dynamics and linear outputs to which known interpolatory model reduction can be applied. This transformation approach is compared with the proposed extension that directly treats quadratic outputs. The comparison shows that transformation hides the problem structure. Numerical examples illustrate that keeping the original QB-QB structure leads to ROMs with better approximation properties.
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Acknowledgements
We thank the reviewers for their comments, which have led to improvements in the presentation.
Funding
This research was supported in part by AFOSR Grant FA9550-22-1-0004, NSF grants CCF-1816219 and DMS-1819144, and by a 2021 National Defense Science and Engineering Graduate (NDSEG) Fellowship Award.
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Appendices
Appendix
A Proof of Theorem 1
We prove Theorem 1. First observe that
By Eq. 13a, there exist vectors \(\textbf{v}_{j, 1} \in \mathbb {C}^r\) such that
Multiplying Eq. 44 by \(\textbf{W}^*{\varvec{\Phi }}(\sigma _j)^{-1}\) yields \(\textbf{W}^*{\varvec{\Phi }}(\sigma _j)^{-1}\textbf{V}\textbf{v}_{j, 1} =\widehat{\varvec{\Phi }}(\sigma _j)^{-1}\textbf{v}_{j, 1} = \textbf{W}^*\textbf{B}\textbf{b}_j = \widehat{\textbf{B}}\textbf{b}_j\). Hence
By equation Eq. 45 and the definition Eq. 44 of \(\textbf{v}_{j,1}\),
which is 14a.
By Eq. 13b, there exist vectors \(\textbf{v}_{j, 2}\in \mathbb {C}^r\) such that
\(j=1, \dots , \ell \).
Next, notice that by the definitions Eqs. 44, 46 of \(\textbf{v}_{j, 1}\) and \(\textbf{v}_{j, 2}\) and Eq. 45,
Hence
\(j=1, \dots , \ell \).
Using Eqs. 47 and 45 and then the definitions Eqs. 44, 46 of \(\textbf{v}_{j, 1}\) and \(\textbf{v}_{j, 2}\) it follows that
which is Eq. 14b.
By Eq. 13c, there exist vectors \(\textbf{w}_{j, 1} \in \mathbb {C}^r\) such that
Multiplying Eq. 48 by \({\varvec{\Phi }}(2\sigma _j)^{-1}\textbf{V}\) yields \(\textbf{w}_{j, 1}^*\textbf{W}^*{\varvec{\Phi }}(2\sigma _j)^{-1}\textbf{V}= \textbf{w}_{j, 1}^*\widehat{\varvec{\Phi }}(2\sigma _j)^{-1} = \textbf{c}_j^*\textbf{C}\textbf{V}= \textbf{c}_j^*\widehat{\textbf{C}}\). Hence,
Using Eqs. 49 and 48 gives
which is Eq. 14c.
By Eq. 13d, there exist vectors \(\textbf{w}_{j, 2} \in \mathbb {C}^r\) such that
In the next equalities we use the identity \(\textbf{M}= \textbf{M}\otimes 1\) for any matrix \(\textbf{M}\) to apply the Kronecker product property Eq. 1. Using Eq. 50, the identity \(\textbf{M}= \textbf{M}\otimes 1\), Eqs. 44, 48, 49, 45 gives
Multiplying on the right by \(\widehat{\varvec{\Phi }}(\sigma _j)=\widehat{\varvec{\Phi }}(\sigma _j)\otimes 1\), we conclude that
Using Eqs. 51 and 50 gives
which is 14d.
B System transformation
The QB-QB system Eq. 2 can be transformed into an equivalent system with linear output, but dynamics with higher nonlinearity. Specifically, we introduce the auxiliary variable
To simplify the following presentation, we again assume that \(\textbf{K}\) is symmetric Eq. 17. Assuming that the input \(\textbf{u}\) is differentiable (if \( \textbf{J}\not = 0\)) and using the symmetry of \(\textbf{K}\) we obtain
The derivative of \(\textbf{x}(t)\) is replaced using Eq. 2a. The QB-QB system Eq. 2 can be equivalently written as
One could rearrange the right hand side in Eq. 53b using Kronecker product properties like Eq. 1. While the transformed system Eq. 53 has a linear output, the price one pays are nonlinear terms of higher order (up to cubic) in the dynamics and the presence (if \( \textbf{J}\not = 0\)) of derivatives of the inputs. In general these additional terms cannot be dealt with easily by current system or interpolation-based model reduction techniques. However, if the original dynamics are linear and the output is quadratic, then the resulting transformed system is a QB system, to which in principle current model reduction techniques can be applied.
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Diaz, A.N., Heinkenschloss, M., Gosea, I.V. et al. Interpolatory model reduction of quadratic-bilinear dynamical systems with quadratic-bilinear outputs. Adv Comput Math 49, 95 (2023). https://doi.org/10.1007/s10444-023-10096-2
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DOI: https://doi.org/10.1007/s10444-023-10096-2