Fine Tuning a Simulation-Driven Estimator
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918306520236032 |
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| author | Lakshminarayanan, Braghadeesh Guerrero, Margarita A. Rojas, Cristian R. |
| author_facet | Lakshminarayanan, Braghadeesh Guerrero, Margarita A. Rojas, Cristian R. |
| contents | Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04480 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine Tuning a Simulation-Driven Estimator Lakshminarayanan, Braghadeesh Guerrero, Margarita A. Rojas, Cristian R. Systems and Control Machine Learning Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations. |
| title | Fine Tuning a Simulation-Driven Estimator |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2504.04480 |