478 research outputs found
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain
(FDTD) simulations to optimize transmission properties of plasmonic mirror
color filters using a multi-objective figure of merit over a five-dimensional
parameter space by utilizing novel multi-fidelity Gaussian processes approach.
We compare these results with conventional derivative-free global search
algorithms, such as (single-fidelity) Gaussian Processes optimization scheme,
and Particle Swarm Optimization---a commonly used method in nanophotonics
community, which is implemented in Lumerical commercial photonics software. We
demonstrate the performance of various numerical optimization approaches on
several pre-collected real-world datasets and show that by properly trading off
expensive information sources with cheap simulations, one can more effectively
optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
arXiv admin note: substantial text overlap with arXiv:1811.0075
Gaussian process based model predictive control : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Engineering and Advanced Technology, Massey University, New Zealand
The performance of using Model Predictive Control (MPC) techniques is highly dependent
on a model that is able to accurately represent the dynamical system. The datadriven
modelling techniques are usually used as an alternative approach to obtain such
a model when first principle techniques are not applicable. However, it is not easy to
assess the quality of learnt models when using the traditional data-driven models, such
as Artificial Neural Network (ANN) and Fuzzy Model (FM). This issue is addressed in
this thesis by using probabilistic Gaussian Process (GP) models.
One key issue of using the GP models is accurately learning the hyperparameters.
The Conjugate Gradient (CG) algorithms are conventionally used in the problem of
maximizing the Log-Likelihood (LL) function to obtain these hyperparameters. In this
thesis, we proposed a hybrid Particle Swarm Optimization (PSO) algorithm to cope with
the problem of learning hyperparameters. In addition, we also explored using the Mean
Squared Error (MSE) of outputs as the fitness function in the optimization problem.
This will provide us a quality indication of intermediate solutions.
The GP based MPC approaches for unknown systems have been studied in the past
decade. However, most of them are not generally formulated. In addition, the optimization
solutions in existing GP based MPC algorithms are not clearly given or are
computationally demanding. In this thesis, we first study the use of GP based MPC approaches
in the unconstrained problems. Compared to the existing works, the proposed
approach is generally formulated and the corresponding optimization problem is eff-
ciently solved by using the analytical gradients of GP models w.r.t. outputs and control
inputs. The GPMPC1 and GPMPC2 algorithms are subsequently proposed to handle
the general constrained problems. In addition, through using the proposed basic and
extended GP based local dynamical models, the constrained MPC problem is effectively
solved in the GPMPC1 and GPMPC2 algorithms. The proposed algorithms are verified
in the trajectory tracking problem of the quadrotor.
The issue of closed-loop stability in the proposed GPMPC algorithm is addressed
by means of the terminal cost and constraint technique in this thesis. The stability
guaranteed GPMPC algorithm is subsequently proposed for the constrained problem. By
using the extended GP based local dynamical model, the corresponding MPC problem
is effectively solved
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization---a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several pre-collected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget
- …