Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911516677111808 |
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| author | Bausback, Ryan Tang, Jingqiao Lu, Lu Bao, Feng Huynh, Toan |
| author_facet | Bausback, Ryan Tang, Jingqiao Lu, Lu Bao, Feng Huynh, Toan |
| contents | We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the Stochastic Neural Network (SNN) with the DeepONet. By formulating the branch net as an SDE and backpropagating through the adjoint BSDE, we replace the gradient of the loss function with the gradient of the Hamiltonian from Stohastic Maximum Principle in the SGD update. This allows SON to learn the uncertainty present in operators through its diffusion parameters. We then demonstrate the effectiveness of SON when replicating several noisy operators in 2D and 3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning Bausback, Ryan Tang, Jingqiao Lu, Lu Bao, Feng Huynh, Toan Machine Learning Probability We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the Stochastic Neural Network (SNN) with the DeepONet. By formulating the branch net as an SDE and backpropagating through the adjoint BSDE, we replace the gradient of the loss function with the gradient of the Hamiltonian from Stohastic Maximum Principle in the SGD update. This allows SON to learn the uncertainty present in operators through its diffusion parameters. We then demonstrate the effectiveness of SON when replicating several noisy operators in 2D and 3D. |
| title | Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2507.10401 |