scholarly journals Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization

2021 ◽  
Author(s):  
Xingfu Wu ◽  
Michael Kruse ◽  
Prasanna Balaprakash ◽  
Hal Finkel ◽  
Paul Hovland ◽  
...  
Keyword(s):  
Bayesian Optimization ◽  
Loop Optimization

scholarly journals Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization

2020 ◽  
Author(s):  
Xingfu Wu ◽  
Michael Kruse ◽  
Prasanna Balaprakash ◽  
Hal Finkel ◽  
Paul Hovland ◽  
...  
Keyword(s):  
Bayesian Optimization ◽  
Loop Optimization

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

Bayesian Optimization for Multi-Objective High-Dimensional Turbine Aero Design

2021 ◽  
Author(s):  
Yiming Zhang ◽  
Sayan Ghosh ◽  
Thomas Vandeputte ◽  
Liping Wang
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Industrial Applications ◽  
Bayesian Optimization ◽  
High Dimensional ◽  
Multi Objective Optimization ◽  
Loop Optimization ◽  
Multi Objective ◽  
Input Design ◽  
Design Variables

Abstract Industrial design fundamentally relies on high-dimensional multi-objective optimization. Bayesian Optimization (BO) based on Gaussian Processes (GPs) has been shown to be effective for this practice where new designs are picked in each iteration for varying objectives including optimization and model refinement. This paper introduces two industrial applications of BO for turbine aero design. The first application is GE’s Aviation & Power DT4D Turbo Aero Design with 32 design variables. It has a single objective to maximize with 32 input/design variables and thus considered high-dimensional in terms of the input space. BO has significantly succeeded the traditional design schemes. It has been shown that finding the maximum-EI points (inner-loop optimization) could be critical and the influence of inner-loop optimization was evaluated. The second application is for multi-objective optimization. Each simulation run is the aggregate result from multiple CFD runs tuning geometry and took 24 hours to complete. BO has been capable to extend the existing Pareto front with a few additional runs. BO has been searching along the border of the design space and therefore motivate the open-up of design space exploration. For both applications, BO successfully guide the CFD run and allocate design variables more optimum than previous design approaches.

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

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.

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