ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically requires significant computational resources. To address this limitation, we propose ELM-DeepONets, a...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11005528/ |
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| author | Hwijae Son |
| author_facet | Hwijae Son |
| author_sort | Hwijae Son |
| collection | DOAJ |
| description | Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically requires significant computational resources. To address this limitation, we propose ELM-DeepONets, an Extreme Learning Machine (ELM) framework for DeepONets that leverages the backpropagation-free nature of ELM. By reformulating DeepONet training as a least-squares problem for newly introduced parameters, the ELM-DeepONet approach significantly reduces training complexity. Validation on benchmark problems, including nonlinear ODEs and PDEs, demonstrates that the proposed method not only achieves superior accuracy but also drastically reduces computational costs. This work offers a scalable and efficient alternative for operator learning in scientific computing. |
| format | Article |
| id | doaj-art-c3fe6fc63e2841c3be987744d03727d5 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c3fe6fc63e2841c3be987744d03727d52025-08-20T02:26:51ZengIEEEIEEE Access2169-35362025-01-0113869278693410.1109/ACCESS.2025.357050211005528ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning MachinesHwijae Son0https://orcid.org/0000-0002-6630-2832Department of Mathematics, Konkuk University, Seoul, South KoreaDeep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically requires significant computational resources. To address this limitation, we propose ELM-DeepONets, an Extreme Learning Machine (ELM) framework for DeepONets that leverages the backpropagation-free nature of ELM. By reformulating DeepONet training as a least-squares problem for newly introduced parameters, the ELM-DeepONet approach significantly reduces training complexity. Validation on benchmark problems, including nonlinear ODEs and PDEs, demonstrates that the proposed method not only achieves superior accuracy but also drastically reduces computational costs. This work offers a scalable and efficient alternative for operator learning in scientific computing.https://ieeexplore.ieee.org/document/11005528/DeepONetsextreme learning machineforward-inverse problems |
| spellingShingle | Hwijae Son ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines IEEE Access DeepONets extreme learning machine forward-inverse problems |
| title | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines |
| title_full | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines |
| title_fullStr | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines |
| title_full_unstemmed | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines |
| title_short | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines |
| title_sort | elm deeponets backpropagation free training of deep operator networks via extreme learning machines |
| topic | DeepONets extreme learning machine forward-inverse problems |
| url | https://ieeexplore.ieee.org/document/11005528/ |
| work_keys_str_mv | AT hwijaeson elmdeeponetsbackpropagationfreetrainingofdeepoperatornetworksviaextremelearningmachines |