R. Freund, H. Men, C. Nguyen, P. Parrilo and J. Peraire

Slides:



Advertisements
Similar presentations
Component Analysis (Review)
Advertisements

Various Regularization Methods in Computer Vision Min-Gyu Park Computer Vision Lab. School of Information and Communications GIST.
Principal Component Analysis Based on L1-Norm Maximization Nojun Kwak IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
P. Venkataraman Mechanical Engineering P. Venkataraman Rochester Institute of Technology DETC2013 – 12269: Continuous Solution for Boundary Value Problems.

Dragan Jovicic Harvinder Singh
Zuse Institute Berlin DFG Research Center MATHEON Finite Element Methods for Maxwell‘s Equations Jan Pomplun, Frank Schmidt Computational Nano-Optics Group.
Manifold Sparse Beamforming
1 Nonlinear Control Design for LDIs via Convex Hull Quadratic Lyapunov Functions Tingshu Hu University of Massachusetts, Lowell.
Graph Laplacian Regularization for Large-Scale Semidefinite Programming Kilian Weinberger et al. NIPS 2006 presented by Aggeliki Tsoli.
Shape From Light Field meets Robust PCA
Limitation of Pulse Basis/Delta Testing Discretization: TE-Wave EFIE difficulties lie in the behavior of fields produced by the pulse expansion functions.
Multi-objective Approach to Portfolio Optimization 童培俊 张帆.
Photonic Crystals and Negative Refraction Dane Wheeler Jing Zhang.
Reformulated - SVR as a Constrained Minimization Problem subject to n+1+2m variables and 2m constrains minimization problem Enlarge the problem size and.
Agilent Technologies Optical Interconnects & Networks Department Photonic Crystals in Optical Communications Mihail M. Sigalas Agilent Laboratories, Palo.
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Santiago, July, 2010 One paper in Journal of Computational Physics, another paper submitted to Physics Review Band.
Data Envelopment Analysis. Weights Optimization Primal – Dual Relations.
Jinhui Tang †, Shuicheng Yan †, Richang Hong †, Guo-Jun Qi ‡, Tat-Seng Chua † † National University of Singapore ‡ University of Illinois at Urbana-Champaign.
IMAM Institute of Mechanics and Advanced Materials
S.S. Yang and J.K. Lee FEMLAB and its applications POSTEC H Plasma Application Modeling Lab. Oct. 25, 2005.
1 Sensitivity Analysis of Narrow Band Photonic-Crystal Waveguides and Filters Ben Z. Steinberg Amir Boag Ronen Lisitsin Svetlana Bushmakin.
The method of moments in dynamic optimization
Some of the applications of Photonic Crystals (by no means a complete overview) Prof. Maksim Skorobogatiy École Polytechnique de Montréal.
Grating reconstruction forward modeling part Mark van Kraaij CASA PhD-day Tuesday 13 November 2007.
Engineering Systems Analysis for Design Richard de Neufville © Massachusetts Institute of Technology Dynamic Programming Slide 1 of 35 Dynamic Programming.
Chapter 24 – Multicriteria Capital Budgeting and Linear Programming u Linear programming is a mathematical procedure, usually carried out by computer software,
MECH345 Introduction to Finite Element Methods Chapter 1 Numerical Methods - Introduction.
Motivation Thus far we have dealt primarily with the input/output characteristics of linear systems. State variable, or state space, representations describe.
ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: Normal Equations The Orthogonality Principle Solution of the Normal Equations.
Large-Scale Matrix Factorization with Missing Data under Additional Constraints Kaushik Mitra University of Maryland, College Park, MD Sameer Sheoreyy.
Finite Element Modelling of Photonic Crystals Ben Hiett J Generowicz, M Molinari, D Beckett, KS Thomas, GJ Parker and SJ Cox High Performance Computing.
STATIC ANALYSIS OF UNCERTAIN STRUCTURES USING INTERVAL EIGENVALUE DECOMPOSITION Mehdi Modares Tufts University Robert L. Mullen Case Western Reserve University.
Application of Finite Element Methods to Photonic Crystal Modelling B.P. Hiett D. Beckett, S.J. Cox, J. Generowicz, M. Molinari, K.S. Thomas High Performance.
Bump Hunting The objective PRIM algorithm Beam search References: Feelders, A.J. (2002). Rule induction by bump hunting. In J. Meij (Ed.), Dealing with.
In this work, we propose a novel local mesh refinement algorithm based on the use of transformation optics. The new algorithm is an alternative way to.
3.6 Solving Systems of Linear Equations in Three Variables ©2001 by R. Villar All Rights Reserved.
Progress Report #2 Alvaro Velasquez. Project Selection I chose to work with Nasim Souly on the project titled “Subspace Clustering via Graph Regularized.
Pareto-Optimality of Cognitively Preferred Polygonal Hulls for Dot Patterns Antony Galton University of Exeter UK.
Principal Component Analysis (PCA)
Support vector machines
Photonic Crystals: Periodic Surprises in Electromagnetism
Four wave mixing in submicron waveguides
Photonic Bandgap (PBG) Concept
examples of dual-mode (3 GHz + 6 GHz) cavities
Ch 12. Continuous Latent Variables ~ 12
R. Freund, H. Men, C. Nguyen, P. Parrilo and J. Peraire
Superconducting Electromagnetic
Polynomial Norms Amir Ali Ahmadi (Princeton University) Georgina Hall
Amir Ali Ahmadi (Princeton University)
Intelligent Information System Lab
Click to edit Master title style
Maksim Skorobogatiy John Joannopoulos MIT, Department of Physics
Chapter 4 Linear Programming: The Simplex Method
Mathematical Programming (towards programming with math)
Support vector machines
Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John.
DALHM Project and Bilkent University
Support Vector Machines
Structural Optimization Design ( Structural Analysis & Optimization )
Support vector machines
Support vector machines
Principal Component Analysis
Nonlinear Dimension Reduction:
Dr. Arslan Ornek DETERMINISTIC OPTIMIZATION MODELS
One-shot learning and generation of dexterous grasps
Chapter 4 . Trajectory planning and Inverse kinematics
Fig. 2 Optomechanical scheme to measure photon angular momentum and optical torque in a waveguide. Optomechanical scheme to measure photon angular momentum.
Sebastian Semper1 and Florian Roemer1,2
Presentation transcript:

R. Freund, H. Men, C. Nguyen, P. Parrilo and J. Peraire Design of Photonic Crystals with Multiple and Combined Band Gaps, plus Fabrication- Robust Design R. Freund, H. Men, C. Nguyen, P. Parrilo and J. Peraire MIT AFOSR, April 2011 MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Wave Propagation in Periodic Media For most l, beam(s) propagate through crystal without scattering (scattering cancels coherently). But for some l (~ 2a), no light can propagate: a band gap scattering ( from S.G. Johnson ) 1887 - Rayleigh 1987 – Yablonovitch & John MASSACHUSETTS INSTITUTE OF TECHNOLOGY

S. G. Johnson et al., Appl. Phys. Lett. 77, 3490 (2000) Photonic Crystals 3D Crystals Band Gap: Objective S. G. Johnson et al., Appl. Phys. Lett. 77, 3490 (2000) Applications By introducing “imperfections” one can develop: Waveguides Hyperlens Resonant cavities Switches Splitters … Mangan, et al., OFC 2004 PDP24 MASSACHUSETTS INSTITUTE OF TECHNOLOGY

The Optimal Design Problem for Photonic Crystals A photonic crystal with optimized 7th band gap. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

The Optimal Design Problem for Photonic Crystals MASSACHUSETTS INSTITUTE OF TECHNOLOGY

The Optimal Design Problem for Photonic Crystals MASSACHUSETTS INSTITUTE OF TECHNOLOGY

The Optimal Design Problem for Photonic Crystals ? MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Previous Work There are some approaches proposed for solving the band gap optimization problem: Cox and Dobson (2000) first considered the mathematical optimization of the band gap problem and proposed a projected generalized gradient ascent method. Sigmund and Jensen (2003) combined topology optimization with the method of moving asymptotes (Svanberg (1987)). Kao, Osher, and Yablonovith (2005) used “the level set” method with a generalized gradient ascent method. However, these earlier proposals are gradient-based methods and use eigenvalues as explicit functions. They suffer from the low regularity of the problem due to eigenvalue multiplicity. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Our Approach Replace original eigenvalue formulation by a subspace method, and Convert the subspace problem to a convex semidefinite program (SDP) via semi-definite inclusion and linearization. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

First Step: Standard Discretizaton MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Optimization Formulation: P0 Typically, MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Parameter Dependence MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Change of Decision Variables MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Reformulating the problem: P1 We demonstrate the reformulation in the TE case, This reformulation is exact, but non-convex and large-scale. We use a subspace method to reduce the problem size. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Subspace Method MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Subspace Method, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Subspace Method, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Linear Fractional SDP : MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Solution Procedure The SDP optimization problems can be solved efficiently using modern interior-point methods [Toh et al. (1999)]. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Optimal Structures MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Computation Time All computation performed on a Linux PC with Dual Core AMD Opteron 270, 2.0 GHz. The eigenvalue problem is solved by using the ARPACK software. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Mesh Adaptivity : Refinement Criteria Interface elements 2:1 rule MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Mesh Adaptivity : Solution Procedure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Optimization Process MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Computation Time MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Multiple Band Gap Optimization Multiple band gap optimization is a natural extension of the previous single band gap optimization that seeks to maximize the minimum of weighted gap-midgap ratios: MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Multiple Band Gap Optimization Even with other things being the same (e.g., discretization, change of variables, subspace construction), the multiple band gap optimization problem cannot be reformulated as a linear fractional SDP. We start with: where MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Multiple Band Gap Optimization This linearizes to: where MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Solution Procedure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Sample Computational Results: 2nd and 5th TM Band Gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Sample Computational Results: 2nd and 5th TM Band Gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Optimized Structures TE polarization, Triangular lattice, 1st and 3rd band gaps TEM polarizations, Square lattice, Complete band gaps, 9th and 12th band gaps MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Pareto Frontiers of 1st and 3rd TE Band Gaps TE polarization MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Results: Computational Time Square lattice Execution Time (minutes) MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Future work Incorporate robust fabrication issues, see next slides…. Design of 3-dimensional photonic crystals, Incorporate robust fabrication issues, see next slides…. Metamaterial designs for cloaking devices, superlenses, and shockwave mitigation. Band-gap design optimization for other wave propagation problems, e.g., acoustic, elastic … Non-periodic structures, e.g., photonic crystal fibers. MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Need for Fabrication Robustness MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Constructing Fabricable Solutions MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Constructing Fabricable Solutions, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Quality of User-Constructed Solution MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Quality of User-Constructed Solution, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Fabrication Robustness Paradigm MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Fabrication Robustness Paradigm, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Basic Results MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Fabrication Robustness: Basic Model MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Computable FR Problems via Special Structure MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Computable FR Problems via Special Structure, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Computable FR Problems via Special Structure, continued MASSACHUSETTS INSTITUTE OF TECHNOLOGY