Design and modeling for computer experiments:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, FL [u.a.]
Chapman & Hall/CRC
2006
|
| Series: | Computer science and data analysis series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xii, 290 p. ill. |
| ISBN: | 1584885467 |
Staff View
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| adam_text | COMPUTER SCIENCE AND DATA ANALYSIS SERIES DESIGN AND MODELING FOR
COMPUTER EXPERIMENTS KAI-TAI FANG HONG KONG BAPTIST UNIVERSITY HONG
KONG, RR. CHINA RUNZE LI THE PENNSYLVANIA STATE UNIVERSITY UNIVERSITY
PARK, PA, U.S.A. AGUS SUDJIANTO BANK OF AMERICA CHARLOTTE, NC, U.S.A.
CHAPMAN & HALL/CRC TAYLOR6TFRANCIS GROUP BOCA RATON LONDON NEW YORK *H
CONTENTS PART I AN OVERVIEW 1 1 INTRODUCTION 3 1.1 EXPERIMENTS AND
TLICIR STATISTICAL DESIGNS 3 1.2 SONIC COIICEPTS IN EXPERIMENTAL DESIGN
4 1.3 COMPUTER EXPERIMENTS 10 1.3.1 MOTIVATIONS 10 1.3.2 METAMODELS 12
1.3.3 COMPUTER EXPERIMENTS IN ENGINEERING 16 1.1 EXAMPLES OF COMPUTER
EXPERIMENTS 20 1.5 SPACE-FILLMG DESIGNS 24 1.6 MODEIMG TECHNIQUES 26 1.7
SENSITIVITY ANALYSIS 31 L.S STRATCGIES FOR COMPUTER EXPERIMENTS ARID AN
ILLUSTRATION CASE STUDY 33 1.9 REMARKS ON COMPUTER EXPERIMENTS 38 1.10
GUIDANCE FOR READING THIS BOOK 40 PART II DESIGNS FOR COMPUTER
EXPERIMENTS 45 2 LATIN HYPERCUBE SARNPLING AND ITS MODIFICATIONS 47 2.1
LATIN HYPERCUBE SARNPLING 17 2.2 RANDOMIZED ORTHOGONAL ARRAY 51 2.3
SYMMETRIE AND ORTHOGONAL COLUMN LATIN HYPEREUBES . . . . 54 2.4 OPTIMAL
LATIN HYPERCUBE DESIGNS 60 2.4.1 IMSE CRITERION 60 2.4.2 ENTROPY
CRITERION 62 2.4.3 MINIMAX AND MAXIMIIR DISTANCE CRIT.ERIA AND THEIR
EXTENSION 64 2.4.1 UNIFORMITY CRITERION 65 3 UNIFORM EXPERIMCNTAL DESIGN
67 3.1 INTRODUCTION 67 3.2 MEASURCS OF UNIFORMITY 68 3.2.1 THE STAR
LP-DISEREPANCY 68 3.2.2 MODIFIED / J2 -DISCREPAIICY 70 3.2.3 THE
CENTERED DISEREPANEY 71 IX X DESIGN AND MODELING FOR COMPUTER
EXPERIMENTS 3.2.4 THE WRAP-AROUND DISCREPANCY 72 3.2.5 A UNIFIED
DEFINITION OF DISCREPANCY 73 3.2.6 DCSCREPANEY FOR CAF.EGORICAL FACTORS
75 3.2.7 APPLICATIONS OF UNIFORMITY IN EXPERIMENTAL DESIGNS . 76 3.3
CONSTRUCTION OF UNIFORM DESIGNS 78 3.3.1 ONE-FACTOR UNIFORM DESIGNS 78
3.3.2 SYMMETRICAL UNIFORM DESIGNS 79 3.3.3 GOORL LATTICE POINT MCTHOCI
80 3.3.4 LATIN SQUARE METHOD 85 3.3.5 EXPANDING ORTHOGONAL ARRAY METHOD
86 3.3.6 THE CUTTING METHOD 86 3.3.7 CONSTRUCTION OF UNIFORM DESIGNS BY
OPTIMIZATION . . 90 3.4 CHARACT.ERISTICS OF THC UNIFORM DESIGN:
ADMISSIBILITY, MINI- MAXJTY, AND ROBUSTNESS 90 3.5 CONSTRUCTION OF
UNIFORM DESIGNS VIA RESOLVABLE BALANCED I.N- COMPLETE BLOCK DESIGNS 93
3.5.1 RESOLVABLE BALANCED INCOMPLETE BLOCK DESIGNS . . . . 93 3.5.2
R.BIBD CONSTRUCTION METHOD 94 3.5.3 NEW UNIFORM DESIGNS 94 3.6
CONSTRUCTION OF ASYMMETRICAL UNIFORM DESIGNS 97 3.6.1 PSEUDO-LCVCL
TECLMIQUE 97 3.6.2 COLLAPSING METHOD 97 3.6.3 COMBINATORIAL METHOD 100
3.6.4 MISCELLANCA 103 4 OPTIMIZATION IN CONSTRUCTION OF DESIGNS FOR
COMPUTER EX- PERIMENTS 105 4.1 OPTIMIZATION PROBLEM IN CONSTRUCTION OF
DESIGNS 105 4.1.1 ALGORITHMIC CONSTRUCTION 106 1.1.2 NEIGHBORHOOD 106
4.1.3 REPLACEMENT RULE 1.07 4.1.4 ITERATION FONNULAE 109 4.2
OPTIMIZATION ALGORITHMS 113 4.2.1 ALGORITHMS 113 4.2.2 LOCAL SEARCH
ALGORITHM 114 4.2.3 SINRULATED ANNEALING ALGORITHM 115 4.2.4 THRESHOLD
ACCEPTING ALGORITHM 115 4.2.5 STOCHASTIC EVOLUTIONARY ALGORITHM 116 4.3
LOWER BOUNDS OF THC DISCREPANCY AND RELATED ALGORITHM . . 117 4.3.1
LOWER BOUNDS OF THE CATEGORICAL DISCREPANCY 119 4.3.2 LOWER BOUNDS OF
THE WRAP-AROUND L2-DISCREPANCY . . 119 4.3.3 LOWER BOUNDS OF THE
CENTCRCD L2-DISCREPANCY . . . . 121 4.3.4 BALANCE-PURSUIT HCURISTIC
ALGORITHM 122 PART III MODELING FOR COMPUTER EXPERIMENTS 125 CONTENTS XI
5 METAMODELING 127 5.1 BASIC CONCEPTS 127 5.1.1 MEAN SQUARE ERROR AND
PREDICTION ERROR 127 5.1.2 REGNLARIZATION 130 5.2 POLYNOMIAL MODELS 133
5.3 SPLINE METHOD 139 5.3.1 CONSTRUETION OF SPLINE BASIS 140 5.3.2 AN
ILLUSTRATION 142 5.3.3 OTHER BASES OF GLOBAL APPROXIMATION 144 5.4
GAUSSIAN KRIGING MODELS 145 5.4.1 PREDICTION VIA KRIGING 146 5.4.2
ESTIMATION OF PARAMETERS 147 5.4.3 A CASE STUDY 153 5.5 BAYESIAN
APPROACH 159 5.5.1 GAUSSIAN PROCESSES 159 5.5.2 BAYESIAN PREDICTION OF
DETERMINIST]C FUNCTIONS . . . . 160 5.5.3 IJSE OF DERIVATIVES IN SURFACE
PREDICTION 162 5.5.4 AN EXAMPLE: BORELIOLE MODEL 165 5.6 NEURAL NETWORK
167 5.6.1 MULTI-LAYER PERCEPTRON NETWORKS 168 5.6.2 A CASE STUDY 172
5.6.3 RADIAL BASIS FUNCTIONS 177 5.7 LOCAL POLYNOMIAL REGRESSION 180
5.7.1 MOTIVATION OF LOCAL POLYNOMIAL REGRESSION 180 5.7.2 METAMODELING
VIA LOCAL POLYNOMIAL REGRESSION . . . . 183 5.8 SOME RECOMMENDATIONS 184
5.8.1 CONNECTIONS 184 5.8.2 RECOMMENDATIONS 185 6 MODEL INTERPRETATION
187 6.1 INTRODUCTION 187 6.2 SENSITIVITY ANALYSIS BASED ON REGRESSION
ANALYSIS 188 6.2.1 CRITCRIA 188 6.2.2 AN EXAMPLE 191 6.3 SENSITIVITY
ANALYSIS BASED ON VARIATION DECONIPOSITION . . . . 193 6.3.1 FUNCTIONAL
ANOVA REPRESENTATION 193 6.3.2 COMPUTATIONAL ISSUES 195 6.3.3 EXAMPLE OF
SOBOP GLOBAL SENSITIVITY 198 6.3.4 CORRCLATION RATIOS AND EXTENSION OF
SOBOL INDICES . . 199 6.3.5 FOURIER AMPLITUDE SENSITIVITY TEST 202
6.3.6 EXAMPLE OF FAST APPLICATION 205 XUE DESIGN AND MODELING FOR
COMPUTER EXPERIMENTS 7 FUNCTIORIAL RESPONSE 207 7.1 COMPUTER EXPERIMENTS
WITH FUNCTIONAL RESPONSE 207 7.2 SPATIAL TEMPORAL MODELS .215 7.2.1
FUNCTIONAL RESPONSE WITH SPARSE SANIPLING RATE ... 215 7.2.2 FUNKTIONAL
RESPONSE WITH INTENSIVE SANIPLING RUTE . . 218 7.3 PENALIZED REGRESSION
SPLINES 219 7.4 FUIIFTIONAL LINEAR MODELS 222 7.1.1 A GRAPHICAL TOOL 223
7.4.2 EFFICICRIT EST.IMATION PROCEDURE 224 7.4.3 AN ILLUSTRATION 226 7.5
SEMIPARAMETRIC REGRESSION MODELS 230 7.5.1 PARTIALLY LINEAR MODEL 230
7.5.2 PARTIALLY FUNCTIONAL LINEAR MODELS 234 7.5.3 AN ILLUSTRATION 236
APPENDIX 241 AT SOME BASIC CONEEPTS IN .MATRIX ALGEBRA 241 A.2 SOME
CONEEPTS IN PROBABILITY AND STATISTICS 244 A.2.1 RANDOM VARIABLESAND
RAIIDOIN VECTORS 244 A.2.2 SOME STATISTICAL DISTRIBUTIONS AND GAUSSIAN
PROEESS . 247 A..3 LINEAR REGRESSION ANALYSIS 249 A.3.1 LINEAR MODELS
250 A.3.2 METHOD OF LEAST SQUARES 251 A.3.3 ANALYSIS OF VARIANTE 252
A.3.4 AN ILLUSTRATION 253 A.4 VARIABLE SELECTION FOR LINEAR REGRESSION
MODELS 256 A.4.1 MONCONVEX PENALIZED LEAST SQUARES 257 A.4.2 ITERATIVELY
RIDGC REGRESSION ALGORITHM 258 A.4.3 AN ILLUSTRATION 259 ACRONYMS 261
REFORENCES 263 INDEX 283 AUTHOR INDEX 287
|
| adam_txt |
COMPUTER SCIENCE AND DATA ANALYSIS SERIES DESIGN AND MODELING FOR
COMPUTER EXPERIMENTS KAI-TAI FANG HONG KONG BAPTIST UNIVERSITY HONG
KONG, RR. CHINA RUNZE LI THE PENNSYLVANIA STATE UNIVERSITY UNIVERSITY
PARK, PA, U.S.A. AGUS SUDJIANTO BANK OF AMERICA CHARLOTTE, NC, U.S.A.
CHAPMAN & HALL/CRC TAYLOR6TFRANCIS GROUP BOCA RATON LONDON NEW YORK *H
CONTENTS PART I AN OVERVIEW 1 1 INTRODUCTION 3 1.1 EXPERIMENTS AND
TLICIR STATISTICAL DESIGNS 3 1.2 SONIC COIICEPTS IN EXPERIMENTAL DESIGN
4 1.3 COMPUTER EXPERIMENTS 10 1.3.1 MOTIVATIONS 10 1.3.2 METAMODELS 12
1.3.3 COMPUTER EXPERIMENTS IN ENGINEERING 16 1.1 EXAMPLES OF COMPUTER
EXPERIMENTS 20 1.5 SPACE-FILLMG DESIGNS 24 1.6 MODEIMG TECHNIQUES 26 1.7
SENSITIVITY ANALYSIS 31 L.S STRATCGIES FOR COMPUTER EXPERIMENTS ARID AN
ILLUSTRATION CASE STUDY 33 1.9 REMARKS ON COMPUTER EXPERIMENTS 38 1.10
GUIDANCE FOR READING THIS BOOK 40 PART II DESIGNS FOR COMPUTER
EXPERIMENTS 45 2 LATIN HYPERCUBE SARNPLING AND ITS MODIFICATIONS 47 2.1
LATIN HYPERCUBE SARNPLING 17 2.2 RANDOMIZED ORTHOGONAL ARRAY 51 2.3
SYMMETRIE AND ORTHOGONAL COLUMN LATIN HYPEREUBES . . . . 54 2.4 OPTIMAL
LATIN HYPERCUBE DESIGNS 60 2.4.1 IMSE CRITERION 60 2.4.2 ENTROPY
CRITERION 62 2.4.3 MINIMAX AND MAXIMIIR DISTANCE CRIT.ERIA AND THEIR
EXTENSION 64 2.4.1 UNIFORMITY CRITERION 65 3 UNIFORM EXPERIMCNTAL DESIGN
67 3.1 INTRODUCTION 67 3.2 MEASURCS OF UNIFORMITY 68 3.2.1 THE STAR
LP-DISEREPANCY 68 3.2.2 MODIFIED / J2 -DISCREPAIICY 70 3.2.3 THE
CENTERED DISEREPANEY 71 IX X DESIGN AND MODELING FOR COMPUTER
EXPERIMENTS 3.2.4 THE WRAP-AROUND DISCREPANCY 72 3.2.5 A UNIFIED
DEFINITION OF DISCREPANCY 73 3.2.6 DCSCREPANEY FOR CAF.EGORICAL FACTORS
75 3.2.7 APPLICATIONS OF UNIFORMITY IN EXPERIMENTAL DESIGNS . 76 3.3
CONSTRUCTION OF UNIFORM DESIGNS 78 3.3.1 ONE-FACTOR UNIFORM DESIGNS 78
3.3.2 SYMMETRICAL UNIFORM DESIGNS 79 3.3.3 GOORL LATTICE POINT MCTHOCI
80 3.3.4 LATIN SQUARE METHOD 85 3.3.5 EXPANDING ORTHOGONAL ARRAY METHOD
86 3.3.6 THE CUTTING METHOD 86 3.3.7 CONSTRUCTION OF UNIFORM DESIGNS BY
OPTIMIZATION . . 90 3.4 CHARACT.ERISTICS OF THC UNIFORM DESIGN:
ADMISSIBILITY, MINI- MAXJTY, AND ROBUSTNESS 90 3.5 CONSTRUCTION OF
UNIFORM DESIGNS VIA RESOLVABLE BALANCED I.N- COMPLETE BLOCK DESIGNS 93
3.5.1 RESOLVABLE BALANCED INCOMPLETE BLOCK DESIGNS . . . . 93 3.5.2
R.BIBD CONSTRUCTION METHOD 94 3.5.3 NEW UNIFORM DESIGNS 94 3.6
CONSTRUCTION OF ASYMMETRICAL UNIFORM DESIGNS 97 3.6.1 PSEUDO-LCVCL
TECLMIQUE 97 3.6.2 COLLAPSING METHOD 97 3.6.3 COMBINATORIAL METHOD 100
3.6.4 MISCELLANCA 103 4 OPTIMIZATION IN CONSTRUCTION OF DESIGNS FOR
COMPUTER EX- PERIMENTS 105 4.1 OPTIMIZATION PROBLEM IN CONSTRUCTION OF
DESIGNS 105 4.1.1 ALGORITHMIC CONSTRUCTION 106 1.1.2 NEIGHBORHOOD 106
4.1.3 REPLACEMENT RULE 1.07 4.1.4 ITERATION FONNULAE 109 4.2
OPTIMIZATION ALGORITHMS 113 4.2.1 ALGORITHMS 113 4.2.2 LOCAL SEARCH
ALGORITHM 114 4.2.3 SINRULATED ANNEALING ALGORITHM 115 4.2.4 THRESHOLD
ACCEPTING ALGORITHM 115 4.2.5 STOCHASTIC EVOLUTIONARY ALGORITHM 116 4.3
LOWER BOUNDS OF THC DISCREPANCY AND RELATED ALGORITHM . . 117 4.3.1
LOWER BOUNDS OF THE CATEGORICAL DISCREPANCY 119 4.3.2 LOWER BOUNDS OF
THE WRAP-AROUND L2-DISCREPANCY . . 119 4.3.3 LOWER BOUNDS OF THE
CENTCRCD L2-DISCREPANCY . . . . 121 4.3.4 BALANCE-PURSUIT HCURISTIC
ALGORITHM 122 PART III MODELING FOR COMPUTER EXPERIMENTS 125 CONTENTS XI
5 METAMODELING 127 5.1 BASIC CONCEPTS 127 5.1.1 MEAN SQUARE ERROR AND
PREDICTION ERROR 127 5.1.2 REGNLARIZATION 130 5.2 POLYNOMIAL MODELS 133
5.3 SPLINE METHOD 139 5.3.1 CONSTRUETION OF SPLINE BASIS 140 5.3.2 AN
ILLUSTRATION 142 5.3.3 OTHER BASES OF GLOBAL APPROXIMATION 144 5.4
GAUSSIAN KRIGING MODELS 145 5.4.1 PREDICTION VIA KRIGING 146 5.4.2
ESTIMATION OF PARAMETERS 147 5.4.3 A CASE STUDY 153 5.5 BAYESIAN
APPROACH 159 5.5.1 GAUSSIAN PROCESSES 159 5.5.2 BAYESIAN PREDICTION OF
DETERMINIST]C FUNCTIONS . . . . 160 5.5.3 IJSE OF DERIVATIVES IN SURFACE
PREDICTION 162 5.5.4 AN EXAMPLE: BORELIOLE MODEL 165 5.6 NEURAL NETWORK
167 5.6.1 MULTI-LAYER PERCEPTRON NETWORKS 168 5.6.2 A CASE STUDY 172
5.6.3 RADIAL BASIS FUNCTIONS 177 5.7 LOCAL POLYNOMIAL REGRESSION 180
5.7.1 MOTIVATION OF LOCAL POLYNOMIAL REGRESSION 180 5.7.2 METAMODELING
VIA LOCAL POLYNOMIAL REGRESSION . . . . 183 5.8 SOME RECOMMENDATIONS 184
5.8.1 CONNECTIONS 184 5.8.2 RECOMMENDATIONS 185 6 MODEL INTERPRETATION
187 6.1 INTRODUCTION 187 6.2 SENSITIVITY ANALYSIS BASED ON REGRESSION
ANALYSIS 188 6.2.1 CRITCRIA 188 6.2.2 AN EXAMPLE 191 6.3 SENSITIVITY
ANALYSIS BASED ON VARIATION DECONIPOSITION . . . . 193 6.3.1 FUNCTIONAL
ANOVA REPRESENTATION 193 6.3.2 COMPUTATIONAL ISSUES 195 6.3.3 EXAMPLE OF
SOBOP GLOBAL SENSITIVITY 198 6.3.4 CORRCLATION RATIOS AND EXTENSION OF
SOBOL' INDICES . . 199 6.3.5 FOURIER AMPLITUDE SENSITIVITY TEST 202
6.3.6 EXAMPLE OF FAST APPLICATION 205 XUE DESIGN AND MODELING FOR
COMPUTER EXPERIMENTS 7 FUNCTIORIAL RESPONSE 207 7.1 COMPUTER EXPERIMENTS
WITH FUNCTIONAL RESPONSE 207 7.2 SPATIAL TEMPORAL MODELS .215 7.2.1
FUNCTIONAL RESPONSE WITH SPARSE SANIPLING RATE . 215 7.2.2 FUNKTIONAL
RESPONSE WITH INTENSIVE SANIPLING RUTE . . 218 7.3 PENALIZED REGRESSION
SPLINES 219 7.4 FUIIFTIONAL LINEAR MODELS 222 7.1.1 A GRAPHICAL TOOL 223
7.4.2 EFFICICRIT EST.IMATION PROCEDURE 224 7.4.3 AN ILLUSTRATION 226 7.5
SEMIPARAMETRIC REGRESSION MODELS 230 7.5.1 PARTIALLY LINEAR MODEL 230
7.5.2 PARTIALLY FUNCTIONAL LINEAR MODELS 234 7.5.3 AN ILLUSTRATION 236
APPENDIX 241 AT SOME BASIC CONEEPTS IN .MATRIX ALGEBRA 241 A.2 SOME
CONEEPTS IN PROBABILITY AND STATISTICS 244 A.2.1 RANDOM VARIABLESAND
RAIIDOIN VECTORS 244 A.2.2 SOME STATISTICAL DISTRIBUTIONS AND GAUSSIAN
PROEESS . 247 A.3 LINEAR REGRESSION ANALYSIS 249 A.3.1 LINEAR MODELS
250 A.3.2 METHOD OF LEAST SQUARES 251 A.3.3 ANALYSIS OF VARIANTE 252
A.3.4 AN ILLUSTRATION 253 A.4 VARIABLE SELECTION FOR LINEAR REGRESSION
MODELS 256 A.4.1 MONCONVEX PENALIZED LEAST SQUARES 257 A.4.2 ITERATIVELY
RIDGC REGRESSION ALGORITHM 258 A.4.3 AN ILLUSTRATION 259 ACRONYMS 261
REFORENCES 263 INDEX 283 AUTHOR INDEX 287 |
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| id | DE-604.BV021561380 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:34:53Z |
| indexdate | 2024-07-09T20:38:40Z |
| institution | BVB |
| isbn | 1584885467 |
| language | English |
| lccn | 2005051751 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014777313 |
| oclc_num | 60882069 |
| open_access_boolean | |
| owner | DE-703 DE-20 |
| owner_facet | DE-703 DE-20 |
| physical | xii, 290 p. ill. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Computer science and data analysis series |
| spelling | Fang, Kaitai 1940- Verfasser (DE-588)124276628 aut Design and modeling for computer experiments Kai-Tai Fang, Runze Li, Agus Sudjianto Boca Raton, FL [u.a.] Chapman & Hall/CRC 2006 xii, 290 p. ill. txt rdacontent n rdamedia nc rdacarrier Computer science and data analysis series Diseño experimental Métamodélisation rasuqam Plan d'expérience Plan d'expérience rasuqam Simulatiemodellen gtt Simulation par ordinateur Simulation par ordinateur rasuqam Computer simulation Experimental design Versuchsplanung (DE-588)4078859-3 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Computersimulation (DE-588)4148259-1 s Versuchsplanung (DE-588)4078859-3 s DE-604 Li, Runze Sonstige (DE-588)129447250 oth Sudjianto, Agus Sonstige oth GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014777313&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fang, Kaitai 1940- Design and modeling for computer experiments Diseño experimental Métamodélisation rasuqam Plan d'expérience Plan d'expérience rasuqam Simulatiemodellen gtt Simulation par ordinateur Simulation par ordinateur rasuqam Computer simulation Experimental design Versuchsplanung (DE-588)4078859-3 gnd Computersimulation (DE-588)4148259-1 gnd |
| subject_GND | (DE-588)4078859-3 (DE-588)4148259-1 |
| title | Design and modeling for computer experiments |
| title_auth | Design and modeling for computer experiments |
| title_exact_search | Design and modeling for computer experiments |
| title_exact_search_txtP | Design and modeling for computer experiments |
| title_full | Design and modeling for computer experiments Kai-Tai Fang, Runze Li, Agus Sudjianto |
| title_fullStr | Design and modeling for computer experiments Kai-Tai Fang, Runze Li, Agus Sudjianto |
| title_full_unstemmed | Design and modeling for computer experiments Kai-Tai Fang, Runze Li, Agus Sudjianto |
| title_short | Design and modeling for computer experiments |
| title_sort | design and modeling for computer experiments |
| topic | Diseño experimental Métamodélisation rasuqam Plan d'expérience Plan d'expérience rasuqam Simulatiemodellen gtt Simulation par ordinateur Simulation par ordinateur rasuqam Computer simulation Experimental design Versuchsplanung (DE-588)4078859-3 gnd Computersimulation (DE-588)4148259-1 gnd |
| topic_facet | Diseño experimental Métamodélisation Plan d'expérience Simulatiemodellen Simulation par ordinateur Computer simulation Experimental design Versuchsplanung Computersimulation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014777313&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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