Parallel Neural Space-Mapping (NSM)Optimization for EM-Based Design

Zhang Chao

Train NSM with 2n+1 sets of data

Example： A Bandpass Filter

Coarse Model: Fine Model:

Example： A Bandpass Filter

1 2 3 1 2 3

T

x S S SL L L

A 5% deviation from X for L and S is used, So there will be 13 sets of data for one iteration.

use Openmp method to get the training data

2 3 1 2 31(1 0.05)T

L S S SL L

1 2 3 1 2 3

T

S S SL L L

1 3 1 2 32(1 0.05)T

L S S SL L

1 2 1 2 33(1 0.05)T

L S S SL L

1 2 3 2 31(1 0.05)

T

S S SL L L

1 2 3 1 32(1 0.05)

T

SS SL L L

1 2 3 1 2 3(1 0.05)

T

SS SL L L

Coarse modelFmapping(w)

Coarse model

L1,L2,L3,S1,S2,S3

Lc1,Lc2,Lc3,Sc1,Sc2,Sc3

freq

X:the input of neural SM model

S21

NSM model

f

fc

𝑓 𝑐=𝑥1× 𝑓𝑟𝑒𝑞+𝑥0

[𝐿𝐶1

𝐿𝐶 2𝐿𝐶3

𝑆𝐶1𝑆𝐶 2

𝑆𝐶3

]=[𝑤11 𝑤12 𝑤13 𝑤14 𝑤15 𝑤16

𝑤21 𝑤22 𝑤23 𝑤24 𝑤25 𝑤26

𝑤31 𝑤32 𝑤33 𝑤34 𝑤35 𝑤36

𝑤41 𝑤42 𝑤43 𝑤44 𝑤45 𝑤46

𝑤51 𝑤52 𝑤53 𝑤54 𝑤55 𝑤56

𝑤61 𝑤62 𝑤63 𝑤64 𝑤65 𝑤66

][𝐿1𝐿2𝐿3𝑆1𝑆2𝑆3

]

Example： A Bandpass Filter

Example： A Bandpass Filter

Coarse modelFmapping(w)

Coarse model

L1,L2,L3,S1,S2,S3

Lc1,Lc2,Lc3,Sc1,Sc2,Sc3

freq

X:the input of neural SM model

S21

fc

f 𝑓 𝑐=𝑥1× 𝑓𝑟𝑒𝑞+𝑥0

Example： A Bandpass Filter

Design Specification:In the passband(4.008GHz-4.058GHz)In the stopband(<3.967GHz,>4.099GHz)

210.95S

210.05S

Example： A Bandpass Filter

The initial state:

The S21 of Coarse Model

The S21 of Fine Model

Example： A Bandpass Filter

Iteration 1: before training

Iteration 1: after training

Iteration 1: value the solution in CST

Before optimization After optimization

Iteration 2: before training

Iteration 2: after training

Iteration 2: value the solution in CST

Before optimization After optimization

Iteration 3: before training

Iteration 3: after training

Iteration 3: value the solution in CST

Before optimization After optimization

Iteration 4: before training

Iteration 4: after training

Iteration 4: value the solution in CST

Before optimization After optimization

Iteration 5: before training

Iteration 5: after training

Iteration 5: value the solution in CST

Before optimization After optimization

A shortcoming of the method

When the error becomes very little, the effect of the method will become very little at the same time. It takes many iterations to let the error disappeared.

So, in the fifth iteration I make the specification more strict.

Example： A Bandpass Filter

Summary:Method NSM Optimization

(use 13 sets of data)CST Optimization

Iterations 5 1258

Average time for one iteration

About 5h 40min:CST time: about 5 min 1,get training data: 4 min 2,evaluation: 1 minADS time: about 5h 35min 1, train the NN: 5h 30min 2, optimization:5min

>38s(for one solver)

Total time: About 28h 20min >13.56h

Thank you！