scholarly journals MUMBO: MUlti-task Max-Value Bayesian Optimization

2021 ◽  
pp. 447-462
Author(s):  
Henry B. Moss ◽  
David S. Leslie ◽  
Paul Rayson
Keyword(s):  
Bayesian Optimization

Theoretical Studies of the Acid-Base Equilibria in a Model Active Site of the Human 20S Proteasome

2020 ◽  
Author(s):  
Jon Uranga ◽  
Lukas Hasecke ◽  
Jonny Proppe ◽  
Jan Fingerhut ◽  
Ricardo A. Mata
Keyword(s):  
Active Site ◽  
Boronic Acid ◽  
Computational Study ◽  
Ubiquitin Proteasome System ◽  
Bayesian Optimization ◽  
Inhibition Mechanism ◽  
20S Proteasome ◽  
Acid Base ◽  
Ubiquitin Proteasome ◽  
Infection Cycles

The 20S Proteasome is a macromolecule responsible for the chemical step in the ubiquitin-proteasome system of degrading unnecessary and unused proteins of the cell. It plays a central role both in the rapid growth of cancer cells as well as in viral infection cycles. Herein, we present a computational study of the acid-base equilibria in an active site of the human proteasome, an aspect which is often neglected despite the crucial role protons play in the catalysis. As example substrates, we take the inhibition by epoxy and boronic acid containing warheads. We have combined cluster quantum mechanical calculations, replica exchange molecular dynamics and Bayesian optimization of non-bonded potential terms in the inhibitors. In relation to the latter, we propose an easily scalable approach to the reevaluation of non-bonded potentials making use of QM/MM dynamics information. Our results show that coupled acid-base equilibria need to be considered when modeling the inhibition mechanism. The coupling between a neighboring lysine and the reacting threonine is not affected by the presence of the inhibitor.

scholarly journals Verifying Controllers Against Adversarial Examples with Bayesian Optimization

2018 ◽  
Author(s):  
Shromona Ghosh ◽  
Felix Berkenkamp ◽  
Gireeja Ranade ◽  
Shaz Qadeer ◽  
Ashish Kapoor
Keyword(s):  
Bayesian Optimization ◽  
Adversarial Examples

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

scholarly journals Efficient Amino Acid Conformer Search with Bayesian Optimization

2021 ◽  
Author(s):  
Lincan Fang ◽  
Esko Makkonen ◽  
Milica Todorović ◽  
Patrick Rinke ◽  
Xi Chen
Keyword(s):  
Amino Acid ◽  
Bayesian Optimization

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

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