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Isight Approximation Pointer

Dr Malik Kayupov | DS Simulia Corp.

Great Lakes Regional Users Meeting

October 12, 2011

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Overview

Motivation – why create approximation models?

Question - Which Isight approximation model gives

the best fit of this data set? RSM? RBF? Kriging?

EBF? OPM? Tuning parameter values?

How it works

Some details of the procedure

Approximation Pointer

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Overview

Motivation – why create approximation models?

Question - Which Isight approximation model gives

the best fit of this data set? RSM? RBF? Kriging?

EBF? OPM? Tuning parameter values?

How it works

Some details of the procedure

Approximation Pointer

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Motivation

Long running codes are not suitable for extensive computer

experiments

In order to conduct design study, a DOE-Approximation

Model-Design Exploration-Validation process is used

Approximation Pointer

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Big Picture – Automated Design Strategies

Y1

Constraint

Boundary

Y2

Initial Design

Make an

Approximation

Model

and Search for

Solution

Robust/Reliability Design

(Quality Engineering)

Feasible Infeasible

(safe) (failed)X2

X1

DOE:

Critical Factors,

Initial Design,

Data Set

Flexible automated design exploration strategies

combining DOE, approximation models, multi-objective

optimization and DFSS

Approximation Pointer

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ExampleAutomotive Crashworthiness Role of vehicle structure for safety

o Absorb/manage the crash energy(through structure deformation)

o Maintaining passenger compartment integrity

Design challenges:o Analysis complexity

• Large deformation / rotation, bending, twisting

• Buckling, cracking, shearing, compression

o Computational complexity• >100,000 element FE model• CPU time: days to weeks per run

o Design studies require many analyses• DOE

• Optimization, multi-objective tradeoffs

• Stochastics: reliability and robustness

o Many crash modes

Approximations / Surrogate models Low fidelity empirical models

Created “bottom-up” from the simulation data

Extremely fast to evaluate

Reasonably accurate

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Motivation

Long running codes are not suitable for extensive computer experiments

In order to conduct design study, a DOE-Approximation Model-Design Exploration-Validation process is used

However, the result depends on the accuracy of the approximation model.

Challenge –

Which approximation model is better?

Is there a better one?

Approximation Pointer

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Overview

Motivation – why create approximation models?

Question - Which Isight approximation model gives

the best fit of this data set? RSM? RBF? Kriging?

EBF? OPM? Tuning parameter values?

How it works

Some details of the procedure

Approximation Pointer

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Isight Approximation Models

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RSM? RBF? EBF? Kriging? OPM?

Tuning parameters?

Which Isight approximation model gives me the best

fit?

Approximation Pointer

Input Parameters Output Parameters

BeamHeight FlangeThickness FlangeWidth WebThickness BeamMass MaximumDeflection MaximumStress

71.58 2.68 56.84 1.42 1.11634544 0.013605933 3.576588413

54.74 2.47 31.58 1.84 0.69338416 0.051128099 9.577362262

46.32 3.11 47.37 1 0.93727592 0.035739432 5.928557525

44.21 4.58 36.32 2.47 1.17394116 0.037466181 5.974288021

48.42 3.32 33.16 4.58 1.15229744 0.04108774 7.208779369

50.53 2.05 60 2.68 1.03721072 0.030469574 5.554294604

77.89 2.26 37.89 2.05 0.90067964 0.025836154 6.044978641

40 1.84 42.63 3.11 0.75553408 0.073960797 10.85847743

65.26 3.53 58.42 4.37 1.86698176 0.011317423 2.724947204

73.68 1.42 53.68 3.53 1.12704592 0.019973612 5.460258565

61.05 5 44.21 4.16 1.8325104 0.013929318 3.086860295

69.47 3.95 30 3.32 1.23595472 0.027988782 5.351855061

80 4.16 48.95 2.89 1.72037376 0.009002552 2.529132474

63.16 1.21 34.74 3.74 0.87146752 0.05819578 11.27438069

52.63 1.63 50.53 5 1.15241784 0.033878717 6.795097939

42.11 3.74 52.11 3.95 1.47439964 0.032076328 4.745204799

75.79 2.89 41.05 4.79 1.60332732 0.01651373 4.024054963

58.95 1 45.79 1.63 0.5163438 0.055498016 12.2429895

56.84 4.79 55.26 2.26 1.78135552 0.013534904 2.759519976

67.37 4.37 39.47 1.21 1.16454828 0.016615638 3.884060273

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Manual approach:

One by one, manually make RBF, EBF, RSM, Kriging , and OPM approximations for variety of the tuning parameters using interactive Isight procedure.

Assess accuracy of every model created for every output parameter and write down the results into a table

Analyze the data table, select the model type and tuning parameter combination that allows the best accuracy

Approximation Pointer

=

Satisfied?

Modify

Model

Assess Approximation

Model Accuracy

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Software Robot approach:

Use the tool described below to automatically make and evaluate RBF, EBF, RSM, Kriging , and OPM approximations for variety of the tuning parameters, and get Isight tasks stuffed with best quality approximation models for every individual output parameter for two fitness criteria

Approximation Pointer

=

Satisfied?

Modify

Model

Assess Approximation

Model Accuracy

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Overview

Motivation – why create approximation models?

Question - Which Isight approximation model gives

the best fit of this data set? RSM? RBF? Kriging?

EBF? OPM? Tuning parameter values?

How it works

Some details of the procedure

Approximation Pointer

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Cross-Validation Process

Approximation Pointer

Input Parameters Output Parameters

BeamHeight FlangeThickness FlangeWidth WebThickness BeamMass MaximumDeflection MaximumStress

71.58 2.68 56.84 1.42 1.11634544 0.013605933 3.576588413

54.74 2.47 31.58 1.84 0.69338416 0.051128099 9.577362262

46.32 3.11 47.37 1 0.93727592 0.035739432 5.928557525

44.21 4.58 36.32 2.47 1.17394116 0.037466181 5.974288021

48.42 3.32 33.16 4.58 1.15229744 0.04108774 7.208779369

50.53 2.05 60 2.68 1.03721072 0.030469574 5.554294604

77.89 2.26 37.89 2.05 0.90067964 0.025836154 6.044978641

40 1.84 42.63 3.11 0.75553408 0.073960797 10.85847743

65.26 3.53 58.42 4.37 1.86698176 0.011317423 2.724947204

73.68 1.42 53.68 3.53 1.12704592 0.019973612 5.460258565

61.05 5 44.21 4.16 1.8325104 0.013929318 3.086860295

69.47 3.95 30 3.32 1.23595472 0.027988782 5.351855061

80 4.16 48.95 2.89 1.72037376 0.009002552 2.529132474

63.16 1.21 34.74 3.74 0.87146752 0.05819578 11.27438069

52.63 1.63 50.53 5 1.15241784 0.033878717 6.795097939

42.11 3.74 52.11 3.95 1.47439964 0.032076328 4.745204799

75.79 2.89 41.05 4.79 1.60332732 0.01651373 4.024054963

58.95 1 45.79 1.63 0.5163438 0.055498016 12.2429895

56.84 4.79 55.26 2.26 1.78135552 0.013534904 2.759519976

67.37 4.37 39.47 1.21 1.16454828 0.016615638 3.884060273

1. Remove a single row from data table

2. Make an approximation model based on the reduced data set

3. Calculate outputs in the point with inputs from the deleted row

4. Compare the model prediction with output values in the deleted row

5. Repeat steps 1 – 4 for another row

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Shooting Competition: 5 shots

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Shot #1

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Shot #2

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Shot #3

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Shot #4

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Shot #5

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All Shot Results

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Winners

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Cross-Validation: Fitness Criteria

Approximation Pointer

x

Y

Ymax

Ymin

Data Points

used to build

the model

Model

DY

Data Point

used in cross-

validation

minmaxmaxmin ,,max

where,meanminmaxmin

YYYY

YFit

FitorFit

error

errorerror

D

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Step-by-Step:Step #1. Get data table in a form of a tab separated text file

Step #2. Open Approximation Pointer in Isight Design Gateway

Approximation Pointer

Step #3. Specify data file name and parameter information in the Task

Step #4. Run the Isight job

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Step-by-Step

Approximation Pointer

Step #5. Get Isight

tasks with optimally

tuned Approximation

models produced as

output file parameters

in the described

above Approximation

Pointer task

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Overview

Motivation – why create approximation models?

Question - Which Isight approximation model gives

the best fit of this data set? RSM? RBF? Kriging?

EBF? OPM? Tuning parameter values?

How it works

Some details of the procedure

Approximation Pointer

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Approximation Pointer: How It Works

Approximation Pointer

History File

Approximation Screener

Approximation Task Builder

Approximation Task

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JAVA CODESThree Java classes: ApproxScreener, ApproxScreenerIsight, ApproxTaskBuilder, and

ReadFile

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Name-and-Value Configuration File Pretty free file format. Parameters are identified by their names in the config.txt file. “ = “ is

used to separate control parameter’s name and value. Example: expression

DataFile = I-Beam_200.txt

sets data file name to “I-Beam_200.txt”

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History File

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Model Selection with EDM

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Demo

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Questions?

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THANK YOU