Emulation of physical processes with Emukit
It addresses the problem of scattered tools and fixed backends in decision-making under uncertainty for researchers and practitioners, but is incremental as it consolidates existing methods into a single toolkit.
The paper introduces Emukit, a Python toolkit for decision making under uncertainty that integrates various machine learning methods like Bayesian optimization and multi-fidelity emulation, and demonstrates its use in three case studies.
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.