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Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
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Protecting SLAs with surrogate models
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems, 2010In this paper, we propose the use of surrogate models to avoid or limit violations of the service level agreements (protect SLAs) of enterprise applications executed within virtualized data centers (VDCs).Modern enterprise services are delivered along with service level agreements (SLAs) that formalize the expected quality of service, and define ...
Alessio Gambi +2 more
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2019
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
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This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
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Water Resources Research, 1972
To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
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To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
openaire +1 more source
Surrogate Models for Coupled Microgrids
2019We consider the operation of coupled microgrids. Each microgrid consists of a number of residential energy systems, each including an energy storage device. The goal is to determine an optimal energy exchange between the microgrids, which results in a two-level optimization problem.
Grundel, S. +2 more
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Introduction to Surrogate Modeling and Surrogate-Based Optimization
2016Surrogate-based optimization (SBO) is the main focus of this book. We provide a brief introduction to the subject in this chapter. In particular, we recall the SBO concept and the optimization flow, discuss the principles of surrogate modeling and typical approaches to construct surrogate models.
Slawomir Koziel, Leifur Leifsson
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The Generalized Penalty-Function/Surrogate Model
Operations Research, 1973This paper combines the monotonic-penalty-function and surrogate models into a general model called the penalty-function/surrogate model. It unifies and generalizes the central theorems of earlier papers, and provides some new theorems that can be specialized to the Lagrangian penalty-function model (GLM) or to linear surrogates.
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Surrogate Models for Antimalarials
1984In recent years studies concerned with the mode of action of antimalarial drugs such as chloroquine have shifted from those concerned primarily with the effects on a particular enzyme or enzyme system to studies concerned with the consequences of the ability of chloroquine to act as a lysosomotropic agent.
S.-C. Chou +3 more
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2019
An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
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An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
openaire +1 more source