# Deep Surrogate of Modular Multi Pump using Active Learning

arXiv: https://arxiv.org/abs/2208.02840 · HTML: https://mlsanity.com/paper/2208.02840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI](https://mlsanity.com/paper/2106.01410/) (2021)
- [Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles](https://mlsanity.com/paper/2111.03140/) (2021)

## Built on by (most cited first, 1 of 1)

- [Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization](https://mlsanity.com/paper/2309.02818/) (2023)

## Abstract (arXiv)

Due to the high cost and reliability of sensors, the designers of a pump reduce the needed number of sensors for the estimation of the feasible operating point as much as possible. The major challenge to obtain a good estimation is the low amount of data available. Using this amount of data, the performance of the estimation method is not enough to satisfy the client requests. To solve this problem of scarcity of data, getting high quality data is important to obtain a good estimation. Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field. In particular we focus on the estimation of the surge distance. We apply Active learning to estimate the surge distance with minimal dataset. Results report that active learning is a valuable technique also for real application.