Heterogeneous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process Article Swipe
Yigitcan Comlek
,
Sandipp Krishnan Ravi
,
Piyush Pandita
,
Sayan Ghosh
,
Liping Wang
,
Wei Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1115/1.4068016
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1115/1.4068016
Artificial intelligence and machine learning frameworks have become powerful tools for establishing computationally efficient mappings between inputs and outputs in engineering problems. These mappings have enabled optimization and analysis routines, leading to innovative designs, advanced material systems, and optimized manufacturing processes. In such modeling efforts, it is common to encounter multiple information (data) sources, each varying in specifications. Data fusion frameworks offer the capability to integrate these diverse sources into unified models, enhancing predictive accuracy and enabling knowledge transfer. However, challenges arise when these sources are heterogeneous, i.e., they do not share the same input parameter space. Such scenarios occur when domains differentiated by complexity such as fidelity, operating conditions, experimental setup, and scale, require distinct parametrizations. To address this challenge, a two-stage heterogeneous multi-source data fusion framework based on the input mapping calibration (IMC) and the latent variable Gaussian process (LVGP) is proposed. In the first stage, the IMC algorithm transforms the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, an LVGP-enabled multi-source data fusion model constructs a single-source-aware surrogate model on the unified reference space. The framework is demonstrated and analyzed through three engineering modeling case studies with distinct challenges: cantilever beams with varying design parametrizations, ellipsoidal voids with varying complexities and fidelities, and Ti6Al4V alloys with varying manufacturing modalities. The results demonstrate that the proposed framework achieves higher predictive accuracy compared to both independent single-source and source-unaware data fusion models.
Related Topics
Concepts
Latent variable
Gaussian process
Computer science
Process (computing)
Latent variable model
Sensor fusion
Variable (mathematics)
Fusion
Latent class model
Data mining
Artificial intelligence
Gaussian
Mathematics
Machine learning
Physics
Linguistics
Operating system
Quantum mechanics
Philosophy
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1115/1.4068016
- OA Status
- green
- Cited By
- 4
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407837783
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407837783Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1115/1.4068016Digital Object Identifier
- Title
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Heterogeneous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian ProcessWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-21Full publication date if available
- Authors
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Yigitcan Comlek, Sandipp Krishnan Ravi, Piyush Pandita, Sayan Ghosh, Liping Wang, Wei ChenList of authors in order
- Landing page
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https://doi.org/10.1115/1.4068016Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.14681801Direct OA link when available
- Concepts
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Latent variable, Gaussian process, Computer science, Process (computing), Latent variable model, Sensor fusion, Variable (mathematics), Fusion, Latent class model, Data mining, Artificial intelligence, Gaussian, Mathematics, Machine learning, Physics, Linguistics, Operating system, Quantum mechanics, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| 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.14681801 |
| primary_location.id | doi:10.1115/1.4068016 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S88457320 |
| primary_location.source.issn | 1050-0472, 1528-9001 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1050-0472 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Mechanical Design |
| primary_location.source.host_organization | https://openalex.org/P4310321320 |
| primary_location.source.host_organization_name | American Society of Mechanical Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310321320 |
| primary_location.source.host_organization_lineage_names | American Society of Mechanical Engineers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Mechanical Design |
| primary_location.landing_page_url | https://doi.org/10.1115/1.4068016 |
| publication_date | 2025-02-21 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4317584387, https://openalex.org/W6860307554, https://openalex.org/W6857788552, https://openalex.org/W3163993681, https://openalex.org/W4288039037, https://openalex.org/W6807635478, https://openalex.org/W6848869710, https://openalex.org/W4407388900, https://openalex.org/W4399779828, https://openalex.org/W4229364280, https://openalex.org/W4321381882, https://openalex.org/W4389165590, https://openalex.org/W2568301464, https://openalex.org/W6678483430, https://openalex.org/W6601848957, https://openalex.org/W4380607278, https://openalex.org/W3111795480, https://openalex.org/W3131493624, https://openalex.org/W2952575862, https://openalex.org/W6782089911, https://openalex.org/W3131512346, https://openalex.org/W2111325674, https://openalex.org/W2794638259, https://openalex.org/W2774341240, https://openalex.org/W2963384660, https://openalex.org/W2115403315, https://openalex.org/W3016474637, https://openalex.org/W3048391136, https://openalex.org/W3104055220, https://openalex.org/W4386091573, https://openalex.org/W4386925850, https://openalex.org/W4281555126, https://openalex.org/W4318542407, https://openalex.org/W3095844360, https://openalex.org/W2531692024, https://openalex.org/W4321638915, https://openalex.org/W2753709519, https://openalex.org/W3086809868, https://openalex.org/W3123459983, https://openalex.org/W4391526982, https://openalex.org/W2302501749, https://openalex.org/W4389566510 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 122, 159, 175 |
| abstract_inverted_index.In | 42, 145, 164 |
| abstract_inverted_index.To | 118 |
| abstract_inverted_index.an | 168 |
| abstract_inverted_index.as | 107 |
| abstract_inverted_index.by | 104 |
| abstract_inverted_index.do | 90 |
| abstract_inverted_index.in | 20, 57 |
| abstract_inverted_index.is | 47, 143, 186 |
| abstract_inverted_index.it | 46 |
| abstract_inverted_index.on | 130, 179 |
| abstract_inverted_index.to | 32, 49, 65, 231 |
| abstract_inverted_index.IMC | 150 |
| abstract_inverted_index.The | 184, 219 |
| abstract_inverted_index.and | 3, 18, 28, 38, 76, 113, 136, 188, 210, 212, 235 |
| abstract_inverted_index.are | 86 |
| abstract_inverted_index.for | 11 |
| abstract_inverted_index.not | 91 |
| abstract_inverted_index.the | 63, 93, 131, 137, 146, 149, 153, 165, 180, 223 |
| abstract_inverted_index.Data | 59 |
| abstract_inverted_index.Such | 98 |
| abstract_inverted_index.both | 232 |
| abstract_inverted_index.case | 194 |
| abstract_inverted_index.data | 126, 171, 237 |
| abstract_inverted_index.each | 55 |
| abstract_inverted_index.have | 7, 25 |
| abstract_inverted_index.into | 70, 158 |
| abstract_inverted_index.same | 94 |
| abstract_inverted_index.such | 43, 106 |
| abstract_inverted_index.that | 222 |
| abstract_inverted_index.they | 89 |
| abstract_inverted_index.this | 120 |
| abstract_inverted_index.when | 83, 101 |
| abstract_inverted_index.with | 196, 201, 207, 215 |
| abstract_inverted_index.(IMC) | 135 |
| abstract_inverted_index.These | 23 |
| abstract_inverted_index.arise | 82 |
| abstract_inverted_index.based | 129 |
| abstract_inverted_index.beams | 200 |
| abstract_inverted_index.first | 147 |
| abstract_inverted_index.i.e., | 88 |
| abstract_inverted_index.input | 95, 132, 155 |
| abstract_inverted_index.model | 173, 178 |
| abstract_inverted_index.occur | 100 |
| abstract_inverted_index.offer | 62 |
| abstract_inverted_index.share | 92 |
| abstract_inverted_index.these | 67, 84 |
| abstract_inverted_index.three | 191 |
| abstract_inverted_index.tools | 10 |
| abstract_inverted_index.voids | 206 |
| abstract_inverted_index.(LVGP) | 142 |
| abstract_inverted_index.(data) | 53 |
| abstract_inverted_index.alloys | 214 |
| abstract_inverted_index.become | 8 |
| abstract_inverted_index.common | 48 |
| abstract_inverted_index.design | 203 |
| abstract_inverted_index.fusion | 60, 127, 172, 238 |
| abstract_inverted_index.higher | 227 |
| abstract_inverted_index.inputs | 17 |
| abstract_inverted_index.latent | 138 |
| abstract_inverted_index.scale, | 114 |
| abstract_inverted_index.second | 166 |
| abstract_inverted_index.setup, | 112 |
| abstract_inverted_index.space. | 97, 163, 183 |
| abstract_inverted_index.spaces | 157 |
| abstract_inverted_index.stage, | 148, 167 |
| abstract_inverted_index.Ti6Al4V | 213 |
| abstract_inverted_index.address | 119 |
| abstract_inverted_index.between | 16 |
| abstract_inverted_index.diverse | 68 |
| abstract_inverted_index.domains | 102 |
| abstract_inverted_index.enabled | 26 |
| abstract_inverted_index.leading | 31 |
| abstract_inverted_index.machine | 4 |
| abstract_inverted_index.mapping | 133 |
| abstract_inverted_index.models, | 72 |
| abstract_inverted_index.models. | 239 |
| abstract_inverted_index.outputs | 19 |
| abstract_inverted_index.process | 141 |
| abstract_inverted_index.require | 115 |
| abstract_inverted_index.results | 220 |
| abstract_inverted_index.sources | 69, 85 |
| abstract_inverted_index.studies | 195 |
| abstract_inverted_index.through | 190 |
| abstract_inverted_index.unified | 71, 160, 181 |
| abstract_inverted_index.varying | 56, 202, 208, 216 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Gaussian | 140 |
| abstract_inverted_index.However, | 80 |
| abstract_inverted_index.accuracy | 75, 229 |
| abstract_inverted_index.achieves | 226 |
| abstract_inverted_index.advanced | 35 |
| abstract_inverted_index.analysis | 29 |
| abstract_inverted_index.analyzed | 189 |
| abstract_inverted_index.compared | 230 |
| abstract_inverted_index.designs, | 34 |
| abstract_inverted_index.distinct | 116, 197 |
| abstract_inverted_index.efforts, | 45 |
| abstract_inverted_index.enabling | 77 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.mappings | 15, 24 |
| abstract_inverted_index.material | 36 |
| abstract_inverted_index.modeling | 44, 193 |
| abstract_inverted_index.multiple | 51 |
| abstract_inverted_index.powerful | 9 |
| abstract_inverted_index.proposed | 224 |
| abstract_inverted_index.sources, | 54 |
| abstract_inverted_index.systems, | 37 |
| abstract_inverted_index.variable | 139 |
| abstract_inverted_index.algorithm | 151 |
| abstract_inverted_index.efficient | 14 |
| abstract_inverted_index.encounter | 50 |
| abstract_inverted_index.enhancing | 73 |
| abstract_inverted_index.fidelity, | 108 |
| abstract_inverted_index.framework | 128, 185, 225 |
| abstract_inverted_index.integrate | 66 |
| abstract_inverted_index.knowledge | 78 |
| abstract_inverted_index.operating | 109 |
| abstract_inverted_index.optimized | 39 |
| abstract_inverted_index.parameter | 96, 156, 162 |
| abstract_inverted_index.problems. | 22 |
| abstract_inverted_index.proposed. | 144 |
| abstract_inverted_index.reference | 161, 182 |
| abstract_inverted_index.routines, | 30 |
| abstract_inverted_index.scenarios | 99 |
| abstract_inverted_index.surrogate | 177 |
| abstract_inverted_index.transfer. | 79 |
| abstract_inverted_index.two-stage | 123 |
| abstract_inverted_index.Artificial | 1 |
| abstract_inverted_index.cantilever | 199 |
| abstract_inverted_index.capability | 64 |
| abstract_inverted_index.challenge, | 121 |
| abstract_inverted_index.challenges | 81 |
| abstract_inverted_index.complexity | 105 |
| abstract_inverted_index.constructs | 174 |
| abstract_inverted_index.frameworks | 6, 61 |
| abstract_inverted_index.innovative | 33 |
| abstract_inverted_index.predictive | 74, 228 |
| abstract_inverted_index.processes. | 41 |
| abstract_inverted_index.transforms | 152 |
| abstract_inverted_index.calibration | 134 |
| abstract_inverted_index.challenges: | 198 |
| abstract_inverted_index.conditions, | 110 |
| abstract_inverted_index.demonstrate | 221 |
| abstract_inverted_index.ellipsoidal | 205 |
| abstract_inverted_index.engineering | 21, 192 |
| abstract_inverted_index.fidelities, | 211 |
| abstract_inverted_index.independent | 233 |
| abstract_inverted_index.information | 52 |
| abstract_inverted_index.modalities. | 218 |
| abstract_inverted_index.LVGP-enabled | 169 |
| abstract_inverted_index.complexities | 209 |
| abstract_inverted_index.demonstrated | 187 |
| abstract_inverted_index.establishing | 12 |
| abstract_inverted_index.experimental | 111 |
| abstract_inverted_index.intelligence | 2 |
| abstract_inverted_index.multi-source | 125, 170 |
| abstract_inverted_index.optimization | 27 |
| abstract_inverted_index.heterogeneous | 124, 154 |
| abstract_inverted_index.manufacturing | 40, 217 |
| abstract_inverted_index.single-source | 234 |
| abstract_inverted_index.differentiated | 103 |
| abstract_inverted_index.heterogeneous, | 87 |
| abstract_inverted_index.source-unaware | 236 |
| abstract_inverted_index.computationally | 13 |
| abstract_inverted_index.specifications. | 58 |
| abstract_inverted_index.parametrizations, | 204 |
| abstract_inverted_index.parametrizations. | 117 |
| abstract_inverted_index.single-source-aware | 176 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.98799183 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |