Multi-task Modeling for Engineering Applications with Sparse Data
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914243443425280 |
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| author | Comlek, Yigitcan Krishnan, R. Murali Ravi, Sandipp Krishnan Moghaddas, Amin Giorjao, Rafael Eff, Michael Samaddar, Anirban Ramachandra, Nesar S. Madireddy, Sandeep Wang, Liping |
| author_facet | Comlek, Yigitcan Krishnan, R. Murali Ravi, Sandipp Krishnan Moghaddas, Amin Giorjao, Rafael Eff, Michael Samaddar, Anirban Ramachandra, Nesar S. Madireddy, Sandeep Wang, Liping |
| contents | Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized by multi-source, multi-fidelity data, addressing challenges of data sparsity and varying task correlations. The proposed framework leverages inter-task relationships across outputs and fidelity levels to improve predictive performance and reduce computational costs. The framework is validated across three representative scenarios: Forrester function benchmark, 3D ellipsoidal void modeling, and friction-stir welding. By quantifying and leveraging inter-task relationships, the proposed MTGP framework offers a robust and scalable solution for predictive modeling in domains with significant computational and experimental costs, supporting informed decision-making and efficient resource utilization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05910 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-task Modeling for Engineering Applications with Sparse Data Comlek, Yigitcan Krishnan, R. Murali Ravi, Sandipp Krishnan Moghaddas, Amin Giorjao, Rafael Eff, Michael Samaddar, Anirban Ramachandra, Nesar S. Madireddy, Sandeep Wang, Liping Machine Learning Applications Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized by multi-source, multi-fidelity data, addressing challenges of data sparsity and varying task correlations. The proposed framework leverages inter-task relationships across outputs and fidelity levels to improve predictive performance and reduce computational costs. The framework is validated across three representative scenarios: Forrester function benchmark, 3D ellipsoidal void modeling, and friction-stir welding. By quantifying and leveraging inter-task relationships, the proposed MTGP framework offers a robust and scalable solution for predictive modeling in domains with significant computational and experimental costs, supporting informed decision-making and efficient resource utilization. |
| title | Multi-task Modeling for Engineering Applications with Sparse Data |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2601.05910 |