DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Proceedings of the 40th annual Design Automation Conference
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Projection-based approaches for model reduction of weakly nonlinear, time-varying systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Performance space modeling for hierarchical synthesis of analog integrated circuits
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Developing design tools for biological and biomedical applications of micro- and nano-technology
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Variational interconnect analysis via PMTBR
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Design and verification of high-speed VLSI physical design
Journal of Computer Science and Technology
Bounding L2 gain system error generated by approximations of the nonlinear vector field
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
A parameterized mask model for lithography simulation
Proceedings of the 46th Annual Design Automation Conference
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In this paper we explore the potential of using a general class offunctional representation techniques, kernel-based regression, inthe nonlinear model reduction problem. The kernel-based view-pointprovides a convenient computational framework for regression,unifying and extending the previously proposed polynomialand piecewise-linear reduction methods. Furthermore, as many familiarmethods for linear system manipulation can be leveraged ina nonlinear context, kernels provide insight into how new, morepowerful, nonlinear modeling strategies can be constructed. Wepresent an SVD-like technique for automatic compression of non-linearmodels that allows systematic identification of model redundanciesand rigorous control of approximation error.