Machine Learning for Surrogate Modeling in Multi-Objective Wing Optimization Article Swipe

View

Clemente Carrari, Gabriel , Pereira Gouveia da Silva, Gabriel ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17904576

This repository contains the code and data developed for a research project supported by São Paulo Research Foundation (FAPESP) under grant number 2024/07804-2 .focusing on aerodynamic multiobjective optimization of subsonic wings using surrogate models based on machine learning. The dataset includes over 2.5 million wing geometries simulated via the Vortex Lattice Method (VLM) with varying geometric parameters and 1.4 million wing geometries simulated via Nonlinear Lifting Line Theory (N-LLT). Machine learning models, including Multi-Layer Perceptrons (MLP), were trained to predict aerodynamic coefficients (CL, CD, CM) with high accuracy, enabling reductions in computational time by approximately three orders of magnitude compared to direct VLM and NLLT simulations. These surrogate models were integrated with optimization algorithms such as Dual Annealing and Genetic Algorithms to perform multiobjective aerodynamic design optimization efficiently. The code facilitates data processing, surrogate model training, aerodynamic performance prediction, and multiobjective optimization workflows, demonstrating the potential of machine learning surrogates in conceptual aircraft design where computational efficiency is critical.

Related Topics

Aerodynamics

Artificial Intelligence

Extreme Learning Machine

Evolutionary Algorithm

Computational Fluid Dynamics

Support Vector Machine

Optimal Design

Concepts

Surrogate model Aerodynamics Artificial intelligence Machine learning Computer science Genetic algorithm Multi-objective optimization Perceptron Stability derivatives Mathematical optimization Extreme learning machine Wing Shape optimization Engineering Algorithm Code (set theory) Global optimization Computation Simulated annealing Optimization problem Uncertainty quantification Evolutionary algorithm Genetic programming Artificial neural network Computational fluid dynamics Support vector machine Stability (learning theory) Nonlinear system Multidisciplinary design optimization Optimal design

Metadata

Type: other
Language: en
Landing Page: https://doi.org/10.5281/zenodo.17904576
OA Status: green
OpenAlex ID: https://openalex.org/W7114922362

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W7114922362

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.5281/zenodo.17904576

Digital Object Identifier
Title: Machine Learning for Surrogate Modeling in Multi-Objective Wing Optimization

Work title
Type: other

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-12-11

Full publication date if available
Authors: Clemente Carrari, Gabriel, Pereira Gouveia da Silva, Gabriel

List of authors in order
Landing page: https://doi.org/10.5281/zenodo.17904576

Publisher landing page
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://doi.org/10.5281/zenodo.17904576

Direct OA link when available
Concepts: Surrogate model, Aerodynamics, Artificial intelligence, Machine learning, Computer science, Genetic algorithm, Multi-objective optimization, Perceptron, Stability derivatives, Mathematical optimization, Extreme learning machine, Wing, Shape optimization, Engineering, Algorithm, Code (set theory), Global optimization, Computation, Simulated annealing, Optimization problem, Uncertainty quantification, Evolutionary algorithm, Genetic programming, Artificial neural network, Computational fluid dynamics, Support vector machine, Stability (learning theory), Nonlinear system, Multidisciplinary design optimization, Optimal design

Top concepts (fields/topics) attached by OpenAlex
Cited by: 0

Total citation count in OpenAlex

Full payload

id	https://openalex.org/W7114922362
doi	https://doi.org/10.5281/zenodo.17904576
ids.doi	https://doi.org/10.5281/zenodo.17904576
ids.openalex	https://openalex.org/W7114922362
fwci
type	other
title	Machine Learning for Surrogate Modeling in Multi-Objective Wing Optimization
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C131675550
concepts[0].level	2
concepts[0].score	0.7732753753662109
concepts[0].wikidata	https://www.wikidata.org/wiki/Q7646884
concepts[0].display_name	Surrogate model
concepts[1].id	https://openalex.org/C13393347
concepts[1].level	2
concepts[1].score	0.6974455714225769
concepts[1].wikidata	https://www.wikidata.org/wiki/Q8424
concepts[1].display_name	Aerodynamics
concepts[2].id	https://openalex.org/C154945302
concepts[2].level	1
concepts[2].score	0.5492609739303589
concepts[2].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[2].display_name	Artificial intelligence
concepts[3].id	https://openalex.org/C119857082
concepts[3].level	1
concepts[3].score	0.5368185043334961
concepts[3].wikidata	https://www.wikidata.org/wiki/Q2539
concepts[3].display_name	Machine learning
concepts[4].id	https://openalex.org/C41008148
concepts[4].level	0
concepts[4].score	0.5154037475585938
concepts[4].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[4].display_name	Computer science
concepts[5].id	https://openalex.org/C8880873
concepts[5].level	2
concepts[5].score	0.4649835526943207
concepts[5].wikidata	https://www.wikidata.org/wiki/Q187787
concepts[5].display_name	Genetic algorithm
concepts[6].id	https://openalex.org/C68781425
concepts[6].level	2
concepts[6].score	0.4330494999885559
concepts[6].wikidata	https://www.wikidata.org/wiki/Q2052203
concepts[6].display_name	Multi-objective optimization
concepts[7].id	https://openalex.org/C60908668
concepts[7].level	3
concepts[7].score	0.42416632175445557
concepts[7].wikidata	https://www.wikidata.org/wiki/Q690207
concepts[7].display_name	Perceptron
concepts[8].id	https://openalex.org/C59681308
concepts[8].level	3
concepts[8].score	0.40443453192710876
concepts[8].wikidata	https://www.wikidata.org/wiki/Q7595727
concepts[8].display_name	Stability derivatives
concepts[9].id	https://openalex.org/C126255220
concepts[9].level	1
concepts[9].score	0.404188871383667
concepts[9].wikidata	https://www.wikidata.org/wiki/Q141495
concepts[9].display_name	Mathematical optimization
concepts[10].id	https://openalex.org/C2780150128
concepts[10].level	3
concepts[10].score	0.4020857512950897
concepts[10].wikidata	https://www.wikidata.org/wiki/Q21948731
concepts[10].display_name	Extreme learning machine
concepts[11].id	https://openalex.org/C97257150
concepts[11].level	2
concepts[11].score	0.38034239411354065
concepts[11].wikidata	https://www.wikidata.org/wiki/Q161358
concepts[11].display_name	Wing
concepts[12].id	https://openalex.org/C29513896
concepts[12].level	3
concepts[12].score	0.358621209859848
concepts[12].wikidata	https://www.wikidata.org/wiki/Q7489239
concepts[12].display_name	Shape optimization
concepts[13].id	https://openalex.org/C127413603
concepts[13].level	0
concepts[13].score	0.35278037190437317
concepts[13].wikidata	https://www.wikidata.org/wiki/Q11023
concepts[13].display_name	Engineering
concepts[14].id	https://openalex.org/C11413529
concepts[14].level	1
concepts[14].score	0.34402623772621155
concepts[14].wikidata	https://www.wikidata.org/wiki/Q8366
concepts[14].display_name	Algorithm
concepts[15].id	https://openalex.org/C2776760102
concepts[15].level	3
concepts[15].score	0.3352918028831482
concepts[15].wikidata	https://www.wikidata.org/wiki/Q5139990
concepts[15].display_name	Code (set theory)
concepts[16].id	https://openalex.org/C164752517
concepts[16].level	2
concepts[16].score	0.333818256855011
concepts[16].wikidata	https://www.wikidata.org/wiki/Q5570875
concepts[16].display_name	Global optimization
concepts[17].id	https://openalex.org/C45374587
concepts[17].level	2
concepts[17].score	0.32972803711891174
concepts[17].wikidata	https://www.wikidata.org/wiki/Q12525525
concepts[17].display_name	Computation
concepts[18].id	https://openalex.org/C126980161
concepts[18].level	2
concepts[18].score	0.3134250342845917
concepts[18].wikidata	https://www.wikidata.org/wiki/Q863783
concepts[18].display_name	Simulated annealing
concepts[19].id	https://openalex.org/C137836250
concepts[19].level	2
concepts[19].score	0.3105476498603821
concepts[19].wikidata	https://www.wikidata.org/wiki/Q984063
concepts[19].display_name	Optimization problem
concepts[20].id	https://openalex.org/C32230216
concepts[20].level	2
concepts[20].score	0.2980321943759918
concepts[20].wikidata	https://www.wikidata.org/wiki/Q7882499
concepts[20].display_name	Uncertainty quantification
concepts[21].id	https://openalex.org/C159149176
concepts[21].level	2
concepts[21].score	0.2980012893676758
concepts[21].wikidata	https://www.wikidata.org/wiki/Q14489129
concepts[21].display_name	Evolutionary algorithm
concepts[22].id	https://openalex.org/C110332635
concepts[22].level	2
concepts[22].score	0.2973567247390747
concepts[22].wikidata	https://www.wikidata.org/wiki/Q629498
concepts[22].display_name	Genetic programming
concepts[23].id	https://openalex.org/C50644808
concepts[23].level	2
concepts[23].score	0.29650411009788513
concepts[23].wikidata	https://www.wikidata.org/wiki/Q192776
concepts[23].display_name	Artificial neural network
concepts[24].id	https://openalex.org/C1633027
concepts[24].level	2
concepts[24].score	0.28628039360046387
concepts[24].wikidata	https://www.wikidata.org/wiki/Q815820
concepts[24].display_name	Computational fluid dynamics
concepts[25].id	https://openalex.org/C12267149
concepts[25].level	2
concepts[25].score	0.28619834780693054
concepts[25].wikidata	https://www.wikidata.org/wiki/Q282453
concepts[25].display_name	Support vector machine
concepts[26].id	https://openalex.org/C112972136
concepts[26].level	2
concepts[26].score	0.279560923576355
concepts[26].wikidata	https://www.wikidata.org/wiki/Q7595718
concepts[26].display_name	Stability (learning theory)
concepts[27].id	https://openalex.org/C158622935
concepts[27].level	2
concepts[27].score	0.2687288820743561
concepts[27].wikidata	https://www.wikidata.org/wiki/Q660848
concepts[27].display_name	Nonlinear system
concepts[28].id	https://openalex.org/C28442783
concepts[28].level	3
concepts[28].score	0.2648284435272217
concepts[28].wikidata	https://www.wikidata.org/wiki/Q3354474
concepts[28].display_name	Multidisciplinary design optimization
concepts[29].id	https://openalex.org/C186394612
concepts[29].level	2
concepts[29].score	0.2552313506603241
concepts[29].wikidata	https://www.wikidata.org/wiki/Q7098942
concepts[29].display_name	Optimal design
keywords[0].id	https://openalex.org/keywords/surrogate-model
keywords[0].score	0.7732753753662109
keywords[0].display_name	Surrogate model
keywords[1].id	https://openalex.org/keywords/aerodynamics
keywords[1].score	0.6974455714225769
keywords[1].display_name	Aerodynamics
keywords[2].id	https://openalex.org/keywords/genetic-algorithm
keywords[2].score	0.4649835526943207
keywords[2].display_name	Genetic algorithm
keywords[3].id	https://openalex.org/keywords/multi-objective-optimization
keywords[3].score	0.4330494999885559
keywords[3].display_name	Multi-objective optimization
keywords[4].id	https://openalex.org/keywords/perceptron
keywords[4].score	0.42416632175445557
keywords[4].display_name	Perceptron
keywords[5].id	https://openalex.org/keywords/stability-derivatives
keywords[5].score	0.40443453192710876
keywords[5].display_name	Stability derivatives
keywords[6].id	https://openalex.org/keywords/extreme-learning-machine
keywords[6].score	0.4020857512950897
keywords[6].display_name	Extreme learning machine
keywords[7].id	https://openalex.org/keywords/wing
keywords[7].score	0.38034239411354065
keywords[7].display_name	Wing
keywords[8].id	https://openalex.org/keywords/shape-optimization
keywords[8].score	0.358621209859848
keywords[8].display_name	Shape optimization
language	en
locations[0].id	doi:10.5281/zenodo.17904576
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400562
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	Zenodo (CERN European Organization for Nuclear Research)
locations[0].source.host_organization	https://openalex.org/I67311998
locations[0].source.host_organization_name	European Organization for Nuclear Research
locations[0].source.host_organization_lineage	https://openalex.org/I67311998
locations[0].license	cc-by
locations[0].pdf_url
locations[0].version
locations[0].raw_type	article
locations[0].license_id	https://openalex.org/licenses/cc-by
locations[0].is_accepted	False
locations[0].is_published
locations[0].raw_source_name
locations[0].landing_page_url	https://doi.org/10.5281/zenodo.17904576
indexed_in	datacite
authorships[0].author.id
authorships[0].author.orcid
authorships[0].author.display_name	Clemente Carrari, Gabriel
authorships[0].countries	BR
authorships[0].institutions[0].id	https://openalex.org/I879563668
authorships[0].institutions[0].ror	https://ror.org/00987cb86
authorships[0].institutions[0].type	education
authorships[0].institutions[0].lineage	https://openalex.org/I879563668
authorships[0].institutions[0].country_code	BR
authorships[0].institutions[0].display_name	Universidade Estadual Paulista (Unesp)
authorships[0].author_position	first
authorships[0].raw_author_name	Clemente Carrari, Gabriel
authorships[0].is_corresponding	True
authorships[1].author.id
authorships[1].author.orcid
authorships[1].author.display_name	Pereira Gouveia da Silva, Gabriel
authorships[1].countries	BR
authorships[1].institutions[0].id	https://openalex.org/I879563668
authorships[1].institutions[0].ror	https://ror.org/00987cb86
authorships[1].institutions[0].type	education
authorships[1].institutions[0].lineage	https://openalex.org/I879563668
authorships[1].institutions[0].country_code	BR
authorships[1].institutions[0].display_name	Universidade Estadual Paulista (Unesp)
authorships[1].author_position	last
authorships[1].raw_author_name	Pereira Gouveia da Silva, Gabriel
authorships[1].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://doi.org/10.5281/zenodo.17904576
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-12-12T00:00:00
display_name	Machine Learning for Surrogate Modeling in Multi-Objective Wing Optimization
has_fulltext	False
is_retracted	False
updated_date	2025-12-12T23:20:42.204495
primary_topic
cited_by_count	0
locations_count	1
best_oa_location.id	doi:10.5281/zenodo.17904576
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400562
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	Zenodo (CERN European Organization for Nuclear Research)
best_oa_location.source.host_organization	https://openalex.org/I67311998
best_oa_location.source.host_organization_name	European Organization for Nuclear Research
best_oa_location.source.host_organization_lineage	https://openalex.org/I67311998
best_oa_location.license	cc-by
best_oa_location.pdf_url
best_oa_location.version
best_oa_location.raw_type	article
best_oa_location.license_id	https://openalex.org/licenses/cc-by
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	https://doi.org/10.5281/zenodo.17904576
primary_location.id	doi:10.5281/zenodo.17904576
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400562
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	Zenodo (CERN European Organization for Nuclear Research)
primary_location.source.host_organization	https://openalex.org/I67311998
primary_location.source.host_organization_name	European Organization for Nuclear Research
primary_location.source.host_organization_lineage	https://openalex.org/I67311998
primary_location.license	cc-by
primary_location.pdf_url
primary_location.version
primary_location.raw_type	article
primary_location.license_id	https://openalex.org/licenses/cc-by
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	https://doi.org/10.5281/zenodo.17904576
publication_date	2025-12-11
publication_year	2025
referenced_works_count	0
abstract_inverted_index.a	9
abstract_inverted_index.as	115
abstract_inverted_index.by	13, 93
abstract_inverted_index.in	90, 150
abstract_inverted_index.is	157
abstract_inverted_index.of	28, 97, 146
abstract_inverted_index.on	24, 35
abstract_inverted_index.to	78, 100, 121
abstract_inverted_index.1.4	58
abstract_inverted_index.2.5	42
abstract_inverted_index.CD,	83
abstract_inverted_index.CM)	84
abstract_inverted_index.The	38, 128
abstract_inverted_index.VLM	102
abstract_inverted_index.and	5, 57, 103, 118, 139
abstract_inverted_index.for	8
abstract_inverted_index.the	3, 48, 144
abstract_inverted_index.via	47, 63
abstract_inverted_index.(CL,	82
abstract_inverted_index.Dual	116
abstract_inverted_index.Line	66
abstract_inverted_index.NLLT	104
abstract_inverted_index.São	14
abstract_inverted_index.This	0
abstract_inverted_index.code	4, 129
abstract_inverted_index.data	6, 131
abstract_inverted_index.high	86
abstract_inverted_index.over	41
abstract_inverted_index.such	114
abstract_inverted_index.time	92
abstract_inverted_index.were	76, 109
abstract_inverted_index.wing	44, 60
abstract_inverted_index.with	53, 85, 111
abstract_inverted_index.(VLM)	52
abstract_inverted_index.Paulo	15
abstract_inverted_index.These	106
abstract_inverted_index.based	34
abstract_inverted_index.grant	20
abstract_inverted_index.model	134
abstract_inverted_index.three	95
abstract_inverted_index.under	19
abstract_inverted_index.using	31
abstract_inverted_index.where	154
abstract_inverted_index.wings	30
abstract_inverted_index.(MLP),	75
abstract_inverted_index.Method	51
abstract_inverted_index.Theory	67
abstract_inverted_index.Vortex	49
abstract_inverted_index.design	125, 153
abstract_inverted_index.direct	101
abstract_inverted_index.models	33, 108
abstract_inverted_index.number	21
abstract_inverted_index.orders	96
abstract_inverted_index.Genetic	119
abstract_inverted_index.Lattice	50
abstract_inverted_index.Lifting	65
abstract_inverted_index.Machine	69
abstract_inverted_index.dataset	39
abstract_inverted_index.machine	36, 147
abstract_inverted_index.million	43, 59
abstract_inverted_index.models,	71
abstract_inverted_index.perform	122
abstract_inverted_index.predict	79
abstract_inverted_index.project	11
abstract_inverted_index.trained	77
abstract_inverted_index.varying	54
abstract_inverted_index.(FAPESP)	18
abstract_inverted_index.(N-LLT).	68
abstract_inverted_index.Research	16
abstract_inverted_index.aircraft	152
abstract_inverted_index.compared	99
abstract_inverted_index.contains	2
abstract_inverted_index.enabling	88
abstract_inverted_index.includes	40
abstract_inverted_index.learning	70, 148
abstract_inverted_index.research	10
abstract_inverted_index.subsonic	29
abstract_inverted_index..focusing	23
abstract_inverted_index.Annealing	117
abstract_inverted_index.Nonlinear	64
abstract_inverted_index.accuracy,	87
abstract_inverted_index.critical.	158
abstract_inverted_index.developed	7
abstract_inverted_index.geometric	55
abstract_inverted_index.including	72
abstract_inverted_index.learning.	37
abstract_inverted_index.magnitude	98
abstract_inverted_index.potential	145
abstract_inverted_index.simulated	46, 62
abstract_inverted_index.supported	12
abstract_inverted_index.surrogate	32, 107, 133
abstract_inverted_index.training,	135
abstract_inverted_index.Algorithms	120
abstract_inverted_index.Foundation	17
abstract_inverted_index.algorithms	113
abstract_inverted_index.conceptual	151
abstract_inverted_index.efficiency	156
abstract_inverted_index.geometries	45, 61
abstract_inverted_index.integrated	110
abstract_inverted_index.parameters	56
abstract_inverted_index.reductions	89
abstract_inverted_index.repository	1
abstract_inverted_index.surrogates	149
abstract_inverted_index.workflows,	142
abstract_inverted_index.Multi-Layer	73
abstract_inverted_index.Perceptrons	74
abstract_inverted_index.aerodynamic	25, 80, 124, 136
abstract_inverted_index.facilitates	130
abstract_inverted_index.performance	137
abstract_inverted_index.prediction,	138
abstract_inverted_index.processing,	132
abstract_inverted_index.2024/07804-2	22
abstract_inverted_index.coefficients	81
abstract_inverted_index.efficiently.	127
abstract_inverted_index.optimization	27, 112, 126, 141
abstract_inverted_index.simulations.	105
abstract_inverted_index.approximately	94
abstract_inverted_index.computational	91, 155
abstract_inverted_index.demonstrating	143
abstract_inverted_index.multiobjective	26, 123, 140
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	2
citation_normalized_percentile