# Emulation of physical processes with Emukit

arXiv: https://arxiv.org/abs/2110.13293 · HTML: https://mlsanity.com/paper/2110.13293/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Survey of multifidelity methods in uncertainty propagation, inference, and optimization](https://mlsanity.com/paper/1806.10761/) (2018)
- [Probabilistic Numerics and Uncertainty in Computations](https://mlsanity.com/paper/1506.01326/) (2015)
- [Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization](https://mlsanity.com/paper/1703.01250/) (2017)
- [GPflowOpt: A Bayesian Optimization Library using TensorFlow](https://mlsanity.com/paper/1711.03845/) (2017)
- [Active Multi-Information Source Bayesian Quadrature](https://mlsanity.com/paper/1903.11331/) (2019)

## Built on by (most cited first, 24 of 24)

- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [Transfer Learning with Gaussian Processes for Bayesian Optimization](https://mlsanity.com/paper/2111.11223/) (2021)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization](https://mlsanity.com/paper/2306.09803/) (2023)
- [Modeling the Machine Learning Multiverse](https://mlsanity.com/paper/2206.05985/) (2022)
- [Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow](https://mlsanity.com/paper/2302.08436/) (2023)
- [ProbNum: Probabilistic Numerics in Python](https://mlsanity.com/paper/2112.02100/) (2021)
- [PyVBMC: Efficient Bayesian inference in Python](https://mlsanity.com/paper/2303.09519/) (2023)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Bayesian Numerical Integration with Neural Networks](https://mlsanity.com/paper/2305.13248/) (2023)
- [AutoML in Heavily Constrained Applications](https://mlsanity.com/paper/2306.16913/) (2023)
- [Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization](https://mlsanity.com/paper/2310.16653/) (2023)
- [Scalable Meta-Learning with Gaussian Processes](https://mlsanity.com/paper/2312.00742/) (2023)
- [Invariant Priors for Bayesian Quadrature](https://mlsanity.com/paper/2112.01578/) (2021)
- [Uncertainty Aware System Identification with Universal Policies](https://mlsanity.com/paper/2202.05844/) (2022)
- [Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors](https://mlsanity.com/paper/2210.17213/) (2022)
- [Fast Model-based Policy Search for Universal Policy Networks](https://mlsanity.com/paper/2202.05843/) (2022)
- [An Active Learning Reliability Method for Systems with Partially Defined Performance Functions](https://mlsanity.com/paper/2210.02168/) (2022)
- [Bayesian Quadrature](https://mlsanity.com/paper/2602.16218/) (2026)
- [Machine Learning Simulates Agent-Based Model Towards Policy](https://mlsanity.com/paper/2203.02576/) (2022)
- [Optimistic Optimization of Gaussian Process Samples](https://mlsanity.com/paper/2209.00895/) (2022)
- [Reproducibility Requires Consolidated Artifacts](https://mlsanity.com/paper/2305.12571/) (2023)
- [Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects](https://mlsanity.com/paper/2504.03943/) (2025)
- [On Some Tunable Multi-fidelity Bayesian Optimization Frameworks](https://mlsanity.com/paper/2508.01013/) (2025)

## Abstract (arXiv)

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has developed a number of methods to facilitate decision making, but so far they are scattered in multiple different toolkits, and generally rely on a fixed backend. In this paper, we present Emukit, a highly adaptable Python toolkit for enriching decision making under uncertainty. Emukit allows users to: (i) use state of the art methods including Bayesian optimization, multi-fidelity emulation, experimental design, Bayesian quadrature and sensitivity analysis; (ii) easily prototype new decision making methods for new problems. Emukit is agnostic to the underlying modeling framework and enables users to use their own custom models. We show how Emukit can be used on three exemplary case studies.