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Engineering Expertise

in the Context of

Computer Guided Design

Workshop «Engineering as Technoscience?»

July 16–17, 2007, University of Duisburg-Essen

Daniel Erni

General and Theoretical Electrical Engineering (ATE)

Faculty of Engineering, University of Duisburg-Essen,

D-47048 Duisburg

Jürg Fröhlich

Laboratory of Electromagnetic Fields and Microwave

Electronics, ETH Zurich, CH-8092 Zurich.

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Introduction I

Towards global search heuristics

What shifts are involved?

Mathematical Model

+ k2( )E

H= 0

Numerical Simulation Search Heuristics

• calculation

• experiments, tinkering

• design

• simulation

• design

• computer guided design

«classical» engineering «new» modes of engineering?

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Introduction II

More Questions than Answers

What happens when engineers start to use

«autonomous» design procedures based on

e.g. evolutionary search heuristics?

Is there an epistemic shift involved in how

engineers will acquire knowledge and expertise in

the described context?

Are the outcomes of such optimization processes

intelligible?

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Introduction III

Outlook

• Example #1: Computer guided design of an optical microcavity.

• Example #2: Ab initio design of an optical microcavity.

• Conjecture: Outcomes may define a novel kind of empirical basis.

• But: What about engineering expertise?

• Conclusion

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Computer Guided Design I

Planar microcavity with accessing waveguides

• Allow energy confinement within an enclosure.

• Energy enhancement in the cavity is tanta-

mount to field enhancement.

• Field enhancement enables enhanced field-

material interactions, and hence functionality.

• Due to field interactions with the fixed boundary,

the energy confinement is effective only for

a very limited frequency range.

• Cavities are good for:

frequency selectivity resonators, filters

light confinement switches, sensors

(1) What are cavities good for?

environment

cavitybarrier,

boundary

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Computer Guided Design II

A. Jebali, R. F. Mahrt, N. Moll, D. Erni, C. Bauer, G.-L. Bona, and W. Bächtold,

J. Appl. Phys., vol. 96, no. 6, 3043, Sept., 2004.

Planar microcavity with accessing waveguides

• classical engineering approach

• numerical structural optimization

(3) The general issue:

plane wave cylindrical wave

conversion?

• simple problem

• difficult to solve

• incoupling outcoupling

(4) Solution strategies:(2) The structure:

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S. Gulde, A. Jebali, N. Moll, Opt. Express,

vol. 13, no. 23, 9502, Oct., 2005.

A. Jebali, D. Erni, S. Gulde, R. F. Mahrt,

W. Bächtold, ICTON 06, June 18-22, 2006.

Q0 = 3994

Q = 594

T = 76 %

FDTD

m1 = 3

m2 = 6

m3 = 11

Planar microcavity…

(A) Classical Engineering Approach:

Decomposition into an inner and

an unperturbed (overall) cavity.

Modularization of the problem.

Direct solution patterns.

(B) Numerical Structural Optimization:

Using a breeder genetic algorithm.

Promising solution after 480 iterations.

Additional task of mode selection

is simultaneously solved.

Q0 = 1892

Q = 368

( 1 ps)

T = 61 %

FEM

m = 1

Computer Guided Design III

Engineers are doomed to wait until

promising solutions are tracked down

by the search heuristics.

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Computer Guided Design IV

Planar microcavity with accessing waveguides

• Engineers are left with the question:

«Why is this solution performing so

well?»

• How can engineers still acquire their

knowledge and expertise?

• Via post-processing:

Making correlations between e.g.

shape and performance.

Setting up classification systems.

(5) Some conclusions:

Engineers have to «re-discover»

the design outcomes.

The outcomes therefore defines

a kind of «novel» empirical basis.

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Outcomes as a novel empirical basis?

Ab initio synthesis of an optical microcavity

(1) Problem setting:

(top view) injected

light fieldRepresentation:

90 90 array of

dielectric material

pixels (white means

low refractive index and

black a high index).

Fitness function

(i.e. the quality measure of the cavity)

Degree of localization,

(i.e. maximal intensity per group of pixels

that are arranged within an square region).

Evolutionary Algorithm

is used here for a nearly

unconstrained search, i.e.

there is a large number

of degrees of freedom.

Prof. Michal Lipson, Cornell University,

A. Gondarenko et al., Phys. Rev. Lett.,

96, 143904 (2006).

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Outcomes as a novel empirical basis?

Ab initio synthesis of an optical microcavity

after 1 iteration

(2) Emergent resonator topology:

after 600 iterations after 700 iterations after 5000 iterations

In the case of a maximally unconstrained search;

what is the quality of such emergent pattern?

Could it be valued as a «natural» outcome?

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Outcomes as a novel empirical basis?

Ab initio synthesis of an optical microcavity

(3) Actual design procedure at Cornell University:

Streamlined

microcavity

structure

«inspiration» actual product

smoothing,

restoring

symmetries,

(i.e. classical

engineering).

Irony: The streamlined microcavity has

become a sort of «bionic» outcome.

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What about Engineering Expertise?

Gedankenexperiment

Assume that based on its success computer guided design schemes (i.e. numerical

structural optimization) have become a cornerstone in modern engineering.

(2) When blackboxing the creative/epistemic process in computer

guided design, how important are classical engineering virtues

(or clichés) such as e.g. imagination?

• Other virtues/skills: Broad awareness of different problem

settings, analogies have to be drawn amongst broader

contexts (towards an «ethnographic» access?).

(1) Will all future engineers have to confine their expertise to

software engineering and operations research?

• Dilemma: «yes» – the unconstrained search emphasizes

the algorithmic aspects of the optimizer.

«no» – optimizers are always strongly context dependent,

and there are still simulators to be designed.

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What about Engineering Expertise?

Gedankenexperiment

(3) How will future engineers acquire their knowledge/skills

if the creative work (design) is all done by computers?

• Post-processing: When operating on the aforementioned

empirical basis engineers will probably behave more like

scientists than like classical engineers.

The post-processing can be formalized and even executed

in parallel to the autonomous design processes. Such kind

of expert system may define a novel realm in future engi-

neering science.

(4) Are the outcomes of computer guided design

scenarios intelligible?

• Yes (by two reasons): (1) Probabilistic search heuristics

may be viewed as formalized tinkering. Find adequate

means of observation for the ongoing optimization process.

(2) Post-processing (cf. above).

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What about Engineering Expertise?

Gedankenexperiment

(5) How will engineers deal with beeing doomed to wait

during a computer guided design procedure?

• Our approach:

Engineers may probably become more flexible and

tackle a variety of different design problems

simultaneously.

The framework of post-processing can even be extended

to incorporate the diversity of all ongoing design scenarios.

There will be «design histories» as well as «design

families» aligned along various projects, all prone to

be explored in order to get better future designs or a

better understanding of the underlying principles.

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Conclusion

Engineering expertise in the context of computer guided design:

• Developing more scientific attitudes towards the various designs.

• Acquiring a broader awareness of the different problem settings.

• Ability to tackle a variety of design problems simultaneously.

• There is an inherent tendency towards multidisciplinarity.

• «Post-processing» may become a realm of ist own in engineering.

Is this all going to happen?

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