Fine Tuning a Simulation-Driven Estimator
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 6 Apr 2025.
Builds on
10 of 26 references.
References · 10
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- 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 45
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- 2023A survey and taxonomy of loss functions in machine learningcited by 15
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- 2022Neural Posterior Estimation with Differentiable Simulatorscited by 9
- 2022Deep transfer learning for system identification using long short-term memory neural networkscited by 4
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- 2022DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical modelscited by 1
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Citing papers per year: 2026: 1.
1 paper.