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Explore techniques and frameworks for moment calculation, change of base projection, and interconnecting passive systems in reduced order modeling. Learn about Krylov subspaces, congruence transformations, and ensuring passivity in interconnected models. Discover methods for system stability and numerical stability in composite simulations. Gain insights into preserving positive semidefiniteness and energy conservation in electrical systems.
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Chapter 2Interconnect Analysis Prof. Lei He Electrical Engineering Department University of California, Los Angeles URL: eda.ee.ucla.edu Email: lhe@ee.ucla.edu
Organization • Chapter 2a First/Second Order Analysis • Chapter 2b Moment calculation and AWE • Chapter 2c Projection based model order reduction
Projection Framework:Change of variables reduced state Note: q << N original state
Projection Framework • Original System • Substitute Note: now few variables (q<<N) in the state, but still thousands of equations (N)
Projection Framework (cont.) Reduction of number of equations: test multiplying by VqT If V and U biorthogonal
Projection Framework (cont.) qxn qxq nxn nxq
Approaches for picking V and U • Use Eigenvectors • Use Time Series Data • Compute • Use the SVD to pick q < k important vectors • Use Frequency Domain Data • Compute • Use the SVD to pick q < k important vectors • Use Krylov Subspace Vectors?
Intuitive view of Krylov subspace choice for change of base projection matrix Taylor series expansion: • change base and use only the first few vectors of the Taylor series expansion: equivalent to match first derivatives around expansion point U
Combine point and moment matching: multipoint moment matching • Multiple expansion points give larger band • Moment (derivates) matching gives more accurate • behavior in between expansion points
Aside on Krylov Subspaces - Definition The order k Krylov subspace generated from matrix A and vector b is defined as
Projection Framework: Moment Matching Theorem (E. Grimme 97) If and Then
Special simple case #1: expansion at s=0,V=U, orthonormal UTU=I If U and V are such that: Then the first q moments (derivatives) of the reduced system match
Need for Orthonormalization of U Vectors will line up with dominant eigenspace!
Need for Orthonormalization of U (cont.) • In "change of base matrix" U transforming to the new reduced state space, we can use ANY columns that span the reduced state space • In particular we can ORTHONORMALIZE the Krylov subspace vectors
For i = 1 to k Generates k+1 vectors! For j = 1 to i Orthogonalize new vector Normalize new vector Orthonormalization of U:The Arnoldi Algorithm
Special case #2: expansion at s=0, biorthogonal VTU=I If U and V are such that: Then the first 2q moments of reduced system match
PVL: Pade Via Lanczos[P. Feldmann, R. W. Freund TCAD95] • PVL is an implementation of the biorthogonal case 2: Use Lanczos process to biorthonormalize the columns of U and V: gives very good numerical stability
Case #3: Intuitive view of subspace choice for general expansion points • In stead of expanding around only s=0 we can expand around another points • For each expansion point the problem can then be put again in the standard form
s2 s1 s1=0 s3 Case #3: Intuitive view of Krylov subspace choice for general expansion points (cont.) Hence choosing Krylov subspace matches first kj of transfer function around each expansion point sj
Interconnected Systems • In reality, reduced models are only useful when connected together with other models and circuit elements in a composite simulation • Consider a state-space model connected to external circuitry (possibly with feedback!) ROM • Can we assure that the simulation of the composite system will be well-behaved? At least preclude non-physical behavior of the reduced model?
Passivity • Passive systems do not generate energy. We cannot extract out more energy than is stored. A passive system does not provideenergy that is not in its storage elements. • If the reduced model is not passive it can generate energy from nothingness and the simulation will explode
- - - - + + + + - - - - + + + + D D D D Q Q Q Q C C C C Interconnecting Passive Systems • The interconnection of stable models is not necessarily stable • BUT the interconnection of passive models is a passive model:
Sufficient conditions for passivity • Sufficient conditions for passivity: Note that these are NOT necessary conditions (common misconception)
Congruence Transformations Preserve Positive Semidefinitness • Def. congruence transformation same matrix • Note: case #1 in the projection framework V=U produces congruence transformations • Property: a congruence transformation preserves the positive semidefiniteness of the matrix • Proof. Just rename • Note:
PRIMA (for preserving passivity) (Odabasioglu, Celik, Pileggi TCAD98) A different implementation of case #1: V=U, UTU=I, Arnoldi Krylov Projection Framework: Use Arnoldi: Numerically very stable
PRIMA preserves passivity • The main difference between and case #1 and PRIMA: • case #1 applies the projection framework to • PRIMA applies the projection framework to • PRIMA preserves passivity because • uses Arnoldi so that U=V and the projection becomes a congruence transformation • E and A produced by electromagnetic analysis are typically positive semidefinite while may not be. • input matrix must be equal to output matrix
Homework 2 (due April 27) • (3) Modify the PRIMA code with single frequency expansion to multiple points expansion. You should use a vector fspan to pass the frequency expansion points. Compare the waveforms of the reduced model between the following two cases: • 1. Single point expansion at s=1e4. • 2. Four-point expansion at s=1e3, 1e5, 1e7, 1e9.
Format of the input matrices for test 1 1 19.4595 1.43391e-14 1 2 0.000464141 -2.9702e-15 1 3 -0.000542882 0.0 1 4 0.000152585 -7.5288e-15 1 5 0.000464074 -2.9702e-15 1 6 -0.000542801 0.0 1 68 -19.4595 0.0 2 1 0.0 -2.9702e-15 2 2 3.66672 2.44291e-13 2 3 0.0 -2.3594e-13 2 4 0.0 -5.3806e-15 2 72 -1.425 0.0 2 329 -2.06075 0.0 2 341 -0.091255 0.0 2 343 -0.0897199 0.0 3 1 -2.44188e-06 0.0 3 2 -0.000464141 -2.3594e-13 3 3 40.8898 2.42089e-13 …..
G,C,B,U,L matrices have been generated. Prima begins: Elapsed time is 2.003787 seconds. Prima done! Calculate original time domain response: Elapsed time is 2.868078 seconds. Original time domain response done! Calculate reduced time domain response: Elapsed time is 0.553771 seconds. Reduced time domain response done! Calculate original frequency response: Elapsed time is 1.192908 seconds. Original frequency response done! Calculate reduced frequency response: Elapsed time is 0.359126 seconds. Reduced frequency response done! Calculate original impulse response: Elapsed time is 0.052804 seconds. Original impulse response done! Calculate reduced impulse response: Elapsed time is 0.052701 seconds. Reduced impulse response done!