# Efficient Calibration for Imperfect Computer Models

arXiv: https://arxiv.org/abs/1507.07280 · HTML: https://mlsanity.com/paper/1507.07280/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2015.

## Built on by (most cited first, 9 of 9)

- [Data Fusion with Latent Map Gaussian Processes](https://mlsanity.com/paper/2112.02206/) (2021)
- [Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set](https://mlsanity.com/paper/2105.12893/) (2021)
- [Rational Kriging](https://mlsanity.com/paper/2312.05372/) (2023)
- [Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner](https://mlsanity.com/paper/2408.00206/) (2024)
- [Epidemiological Model Calibration via Graybox Bayesian Optimization](https://mlsanity.com/paper/2412.07193/) (2024)
- [Digital Twin Calibration with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2501.02205/) (2025)
- [Accounting for Uncertainty in Machine Learning Surrogates: A Gauss-Hermite Quadrature Approach to Reliability Analysis](https://mlsanity.com/paper/2509.18128/) (2025)
- [APIC: Amortized Physics-Informed Calibration using Neural Processes](https://mlsanity.com/paper/2606.03355/) (2026)
- [A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification](https://mlsanity.com/paper/2606.28871/) (2026)

## Abstract (arXiv)

Many computer models contain unknown parameters which need to be estimated using physical observations. Kennedy and O'Hagan (2001) shows that the calibration method based on Gaussian process models proposed by Kennedy and O'Hagan (2001) may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.