Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process Article Swipe

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Yigitcan Comlek , Sandipp Krishnan Ravi , Piyush Pandita , Sayan Ghosh , Liping Wang , Wei Chen ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2407.11268

Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have enabled optimization and analysis routines that have warranted superior designs, ingenious material systems and optimized manufacturing processes. A common occurrence in such modeling endeavors is the existence of multiple source of data, each differentiated by fidelity, operating conditions, experimental conditions, and more. Data fusion frameworks have opened the possibility of combining such differentiated sources into single unified models, enabling improved accuracy and knowledge transfer. However, these frameworks encounter limitations when the different sources are heterogeneous in nature, i.e., not sharing the same input parameter space. These heterogeneous input scenarios can occur when the domains differentiated by complexity, scale, and fidelity require different parametrizations. Towards addressing this void, a heterogeneous multi-source data fusion framework is proposed based on input mapping calibration (IMC) and latent variable Gaussian process (LVGP). In the first stage, the IMC algorithm is utilized to transform the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, a multi-source data fusion model enabled by LVGP is leveraged to build a single source-aware surrogate model on the transformed reference space. The proposed framework is demonstrated and analyzed on three engineering case studies (design of cantilever beam, design of ellipsoidal void and modeling properties of Ti6Al4V alloy). The results indicate that the proposed framework provides improved predictive accuracy over a single source model and transformed but source unaware model.

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Latent variable Gaussian process Process (computing) Fusion Computer science Variable (mathematics) Latent variable model Gaussian Data mining Artificial intelligence Pattern recognition (psychology) Algorithm Mathematics Physics Quantum mechanics Linguistics Mathematical analysis Philosophy Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2407.11268
PDF: https://arxiv.org/pdf/2407.11268
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Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2407.11268

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Title: Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-07-15

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Authors: Yigitcan Comlek, Sandipp Krishnan Ravi, Piyush Pandita, Sayan Ghosh, Liping Wang, Wei Chen

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Landing page: https://arxiv.org/abs/2407.11268

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Concepts: Latent variable, Gaussian process, Process (computing), Fusion, Computer science, Variable (mathematics), Latent variable model, Gaussian, Data mining, Artificial intelligence, Pattern recognition (psychology), Algorithm, Mathematics, Physics, Quantum mechanics, Linguistics, Mathematical analysis, Philosophy, Operating system

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Cited by: 1

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abstract_inverted_index.efficient	10
abstract_inverted_index.encounter	89
abstract_inverted_index.endeavors	45
abstract_inverted_index.existence	48
abstract_inverted_index.fidelity,	57
abstract_inverted_index.framework	134, 199, 229
abstract_inverted_index.ingenious	32
abstract_inverted_index.knowledge	84
abstract_inverted_index.leveraged	184
abstract_inverted_index.operating	58
abstract_inverted_index.optimized	36
abstract_inverted_index.parameter	105, 163, 169
abstract_inverted_index.problems.	18
abstract_inverted_index.reference	168, 195
abstract_inverted_index.scenarios	110
abstract_inverted_index.surrogate	190
abstract_inverted_index.transfer.	85
abstract_inverted_index.transform	159
abstract_inverted_index.warranted	29
abstract_inverted_index.Artificial	0
abstract_inverted_index.addressing	126
abstract_inverted_index.cantilever	211
abstract_inverted_index.frameworks	5, 66, 88
abstract_inverted_index.occurrence	41
abstract_inverted_index.predictive	232
abstract_inverted_index.processes.	38
abstract_inverted_index.properties	219
abstract_inverted_index.calibration	141
abstract_inverted_index.complexity,	118
abstract_inverted_index.conditions,	59, 61
abstract_inverted_index.ellipsoidal	215
abstract_inverted_index.engineering	17, 206
abstract_inverted_index.limitations	90
abstract_inverted_index.possibility	70
abstract_inverted_index.transformed	194, 240
abstract_inverted_index.demonstrated	201
abstract_inverted_index.experimental	60
abstract_inverted_index.intelligence	1
abstract_inverted_index.multi-source	131, 176
abstract_inverted_index.optimization	23
abstract_inverted_index.source-aware	189
abstract_inverted_index.heterogeneous	96, 108, 130, 161
abstract_inverted_index.manufacturing	37
abstract_inverted_index.differentiated	55, 74, 116
abstract_inverted_index.computationally	9
abstract_inverted_index.parametrizations.	124
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
citation_normalized_percentile