# Active emulation of computer codes with Gaussian processes -- Application to remote sensing

arXiv: https://arxiv.org/abs/1912.06552 · HTML: https://mlsanity.com/paper/1912.06552/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 11 of 11)

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- [Compressed particle methods for expensive models with application in Astronomy and Remote Sensing](https://mlsanity.com/paper/2107.08465/) (2021)
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- [Inference over radiative transfer models using variational and expectation maximization methods](https://mlsanity.com/paper/2204.03346/) (2022)
- [Robust Bayesian Optimization via Localized Online Conformal Prediction](https://mlsanity.com/paper/2411.17387/) (2024)
- [Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions](https://mlsanity.com/paper/2104.08134/) (2021)
- [Gaussian Process Classification Bandits](https://mlsanity.com/paper/2212.13157/) (2022)
- [An automatic counting algorithm for the quantification and uncertainty analysis of the number of microglial cells trainable in small and heterogeneous datasets](https://mlsanity.com/paper/2602.22974/) (2026)

## Abstract (arXiv)

Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations, model tractability, as well as compact and expressive simulated datasets. In order to achieve this, the proposed Active Multi-Output Gaussian Process Emulator (AMOGAPE) combines the predictive capacity of Gaussian Processes (GPs) with the design of an acquisition function that favors sampling in low density and fluctuating regions of the approximation functions. Comparing different acquisition functions, we illustrate the promising performance of the method for the construction of emulators with toy examples, as well as for a widely used remote sensing transfer code.