A Multivariate Bayesian Optimization Framework for Long-Term Controller Adaptation in Artificial Pancreas

2018 ◽  
Author(s):  
Dawei Shi ◽  
Eyal Dassau ◽  
Francis J. Doyle
Keyword(s):  
Artificial Pancreas ◽  
Bayesian Optimization ◽  
Optimization Framework

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.

Using Bayesian Optimization With Knowledge Transfer for High Computational Cost Design: A Case Study in Photovoltaics

2019 ◽  
Author(s):  
Mine Kaya ◽  
Shima Hajimirza
Keyword(s):  
Solar Cells ◽  
Design Problem ◽  
Cost Optimization ◽  
Computational Cost ◽  
Design Procedure ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Previous Design ◽  
High Computational Cost

Abstract Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.

scholarly journals A Bayesian Optimization Framework for Neural Network Compression

2019 ◽  
Author(s):  
Xingchen Ma ◽  
Amal Rannen Triki ◽  
Maxim Berman ◽  
Christos Sagonas ◽  
Jacques Cali ◽  
...  
Keyword(s):  
Neural Network ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Network Compression

scholarly journals Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Dimensional Parameter ◽  
Optimization Framework ◽  
Individual Based Models ◽  
Objective Parameter ◽  
History Of

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

scholarly journals Designing staggered platelet composite structure with Gaussian process regression based Bayesian optimization

2021 ◽  
Author(s):  
Kundo Park ◽  
Youngsoo Kim ◽  
Minki Kim ◽  
Chihyeon Song ◽  
Jinkyoo Park ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Composite Structure ◽  
Optimization Problems ◽  
Gaussian Process Regression ◽  
Monte Carlo Algorithm ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Uniaxial Tensile ◽  
Initial Training ◽  
Optimization Framework

The staggered platelet composite structure, one of the most well-known examples of biomimetics, is inspired by the microstructure of nacre, where stiff mineral platelets are stacked with a small fraction of soft polymer in a brick-and-mortar style. Significant efforts have been made to establish a framework for designing a staggered platelet pattern that achieves an excellent balance of toughness and stiffness. However, because no analytical formula for accurately predicting its toughness is available because of the complexity of the failure mechanism of realistic composites, existing studies have investigated either idealized composites with simplified material properties or realistic composites designed by heuristics. In the present study, we propose a Bayesian optimization framework to design a staggered platelet structure that renders high toughness. Gaussian process regression (GPR) was adopted to model statistically the complex relationship between the shape of the staggered platelet array and the resultant toughness. The Markov chain Monte Carlo algorithm was used to determine the optimal kernel hyperparameter set for the GPR. Starting with 14 initial training data collected with uniaxial tensile tests, a GPR-based Bayesian optimization using the expected improvement (EI) acquisition function was carried out. As a result, it was possible to design a staggered platelet pattern with a toughness 11% higher than that of the best sample in the initial training set, and this improvement was achieved after only three iterations of our optimization cycle. As this optimization framework does not require any material theories and models, this process can be easily adapted and applied to various other material optimization problems based on a limited set of experiments or computational simulations.

scholarly journals Multivariate learning framework for long-term adaptation in the artificial pancreas

2018 ◽  
Vol 4 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Dawei Shi ◽  
Eyal Dassau ◽  
Francis J. Doyle
Keyword(s):  
Artificial Pancreas ◽  
Learning Framework

Biohybrid Artificial Pancreas Long-Term Insulin Secretion by Devices Seeded with Canine Islets

Diabetes ◽  
1985 ◽  
Vol 34 (9) ◽  
pp. 850-854 ◽  
Author(s):  
Y. Araki ◽  
B. A. Solomon ◽  
R. M. Basile ◽  
W. L. Chick
Keyword(s):  
Insulin Secretion ◽  
Artificial Pancreas
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