$$\newcommand{\vb}{\mathbf} \newcommand{\wt}{\widetilde} \newcommand{\mc}{\mathcal} \newcommand{\bmc}{\boldsymbol{\mathcal{#1}}} \newcommand{\sup}{^{\text{#1}}} \newcommand{\sups}{^{\text{#1}}} \newcommand{\sub}{_{\text{#1}}} \newcommand{\subs}{_{\text{#1}}} \newcommand{\pard}{\frac{\partial #1}{\partial #2}} \newcommand{\VMV}{ \Big\langle #1 \Big| #2 \Big| #3 \Big\rangle}$$

meep.adjoint: Adjoint sensitivity analysis for automated design optimization¶

This section of the meep documentation covers meep.adjoint, a submodule of the meep python module that implements an adjoint-based sensitivity solver to facilitate automated design optimization via derivative-based numerical optimizers. table of contents

The meep.adjoint documentation is divided into a number of subsections:

• This Overview page reviews some basic facts about adjoints and optimizers, outlines the steps needed to prepare a meep geometry for optimization, and sketches the mechanics of the meep.adjoint design process. (This page is designed to be a gentle introduction for the adjoint neophyte; experts may want only to skim it before skipping to the next section.)

• The Reference Manual fills in the details of the topics outlined on this page, spelling out exactly how to write the python script that will drive your optimization session.

• The Example Gallery presents a number of worked examples that illustrate how meep.adjoint tackles practical problems in various settings.

• The Implementation Notes page offers a glimpse of what's behind the hood---the physical and mathematical basis of the adjoint method and how they are implemented by meep.adjoint. An understanding of this content is not strictly necessary to use the solver, but may help you get more out of the process.

• Although logically independent of the adjoint solver, the Visualization package bundled with the meep.adjoint module offers several general-purpose utilities for convenient visualization of various aspects of meep calculations, which are useful in any meep calculation whether adjoint-related or not.

A common task in electromagnetic engineering is to custom-tune the design of some component of a system---a waveguide taper, a power splitter, an input coupler, an antenna, etc.---to optimize the performance of the system as defined by some problem-specific metric. For our purposes, a "design" will consist of a specification of the spatially-varying scalar permittivity $\epsilon(\mathbf x)$ in some subregion of a meep geometry, and the performance metric will be a physical quantity computed from frequency-domain fields---a power flux, an energy density, an eigenmode expansion coefficient, or perhaps some mathematical function of one or more of these quantities. We will shortly present a smorgasbord of examples; for now, perhaps a good one to have in mind is the hole cloak discussed below, in which a chunk of material has been removed from an otherwise perfect waveguide section, ruining the otherwise perfectly unidirectional (no scattering or reflection) flow of power from a source at one end of the guide to a sink at the other; our task is to tweak the permittivity in an annular region surrounding the defect (the cloak) so as to restore as much as possible the reflectionless transfer of power across the waveguide---thus hiding or "cloaking" the presence of defect from external detection.

Now, given a candidate design $\epsilon\sup{trial}(\mathbf{x})$, it's easy enough to see how we can use meep to evaluate its performance---just create a meep geometry with $\epsilon\sup{trial}$ as a spatially-varying permittivity function, in the design region, add DFT cells to tabulate the frequency-domain Poynting flux entering and departing the cloak region, timestep until the DFTs converge, then use post-processing routines like get_fluxes(') or perhaps get_eigenmode_coefficients() to get the quantities needed to evaluate the performance of the device. Thus, for the cost of one full meep timestepping run we obtain the value of our objective function at one point in the parameter space of possible inputs.

But now what do we do?! The difficulty is that the computation izinust described furnishes only the value of the objective function for a given input, not its derivatives with respect to the design variables---and thus yields zero insight into how we should tweak the design to improve performance. In simple cases we might hope to proceed on the basis of physical intuition, while for small problems with just a few parameters we might try our luck with a derivative-free optimization algorithm; however, both of these approaches will run out of steam long before we scale up to the full complexity of a practical problem with thousands of degrees of freedom. Alternatively, we could get approximate derivative information by brute-force finite-differencing---slightly tweaking one design variable, repeating the full timestepping run, and asking how the results changed---but proceeding this way to compute derivatives with respect to all $D$ design variables would require fully $D$ separate timestepping runs; for the problem sizes we have in mind, this would make calculating the objective-function gradient several thousand times more costly than calculating its value. So we face a dilemma: How can we obtain the derivative information necessary for effective optimization in a reasonable amount of time? This is where adjoints come to the rescue.

The adjoint method of sensitivity analysis is a technique in which we exploit certain facts about the physics of a problem and the consequent mathematical structure---specifically, in this case, the linearity and reciprocity of Maxwell's equations---to rearrange the calculation of derivatives in a way that yields an enormous speedup over the brute-force finite-difference approach. More specifically, after we have computed the objective-function value by doing the full meep timestepping run mentioned above---the "forward" run in adjoint-method parlance---we can magically compute its derivatives with respect to all design variables by doing just one additional timestepping run with a funny-looking choice of sources and outputs (the "adjoint" run). Thus, whereas gradient computation via finite-differencing is at least $D$ times more expensive than computing the objective function value, with adjoints we get both value and gradient for roughly just twice the cost of the value alone. Such a bargain! At this modest cost, derivative-based optimization becomes entirely feasible. More general materials Although for simplicity we focus here on case of isotropic, non-magnetic materials, the adjoint solver is also capable of optimizing geometries involving permeable ($\mu\ne 1$) and anisotropic (tensor-valued $\boldsymbol{\epsilon},\boldsymbol{\mu}$) media.

Examples of optimization problems¶

Throughout the meep.adjoint documentation we will refer to a running collection of simple optimization problems to illustrate the mechanics of optimization, among which are the following; click the geometry images to view in higher resolution.

The Holey Waveguide¶

By way of warm-up, a useful toy version of an optimization problem is an otherwise pristine length of dielectric slab waveguide in which a careless technician has torn a circular hole of variable permittivity $\epsilon\sup{hole}$.  Incident power from an eigenmode source (cyan line in figure) travels leftward through the waveguide, but is partially reflected by the hole, resulting in less than 100% power the waveguide output (as may be characterized in meep by observing power flux and/or eigenmode expansion coefficients at the two flux monitors, labeled east and west). Our objective is to tweak the value of $\epsilon\sup{hole}$ to maximize transmission as assessed by one of these metrics. The simplicity of this model makes it a useful initial warm-up and sanity check for making sure we know what we are doing in design optimization; for example, in this worked example we use it to confirm the numerical accuracy of adjoint-based gradients computed by mp.adjoint

The Hole Cloak¶

We obtain a more challenging variant of the holey-waveguide problem be supposing that the material in the hole region is not a tunable design parameter---it is fixed at vacuum, say, or perfect metal---but that we are allowed to vary the permittivity in an annular region surrounding the hole in such a way as to mimic the effect of filling in the hole, i.e. of hiding or "cloaking" the hole as much as possible from external detection.  For the hole-cloak optimization, the objective function will most likely the same as that considered above---namely, to maximize the Poynting flux through the flux monitor labeled east (a quantity we label $S\subs{east}$) or perhaps to maximize the overlap coefficient between the actual fields passing through monitor east and the fields of (say) the $n$th forward- or backward-traveling eigenmode of the waveguide (which we label $\{P,M\}_{n,\text{east}}$ with $P,M$ standing for "plus and minus.") On the other hand, the design space here is more complicated than for the simple hole, consisting of all possible scalar functions $\epsilon(r,\theta)$ defined on the annular cloak region.

The cross-router¶

A different flavor of waveguide-optimization problem arises when we consider the routing of signals from given inputs to given destinations. One example is the cross-router, involving an intersection between $x-$directed and $y-$directed waveguides, with center region of variable permittivity that we may tweak to control the routing of power through it.  Whereas in the previous examples there was more or less only one reasonable design objective one might realistically want to optimize, for a problem like this there are many possibilities. For example, given fixed input power supplied by an eigenmode source on the "western" branch (cyan line), we might be less interested in the absolute output power at any port and more concerned with achieving maximal equality of output power among the north, south, and east outputs, whereupon we might minimize an objective function of the form $$f\sub{obj} = \Big( S\sub{north} - S\sub{south}\Big)^2 +\Big( S\sub{north} - S\sub{east}\Big)^2 + \Big( S\sub{east} - S\sub{south}\Big)^2$$ (or a similar functional form involving eigenmode coefficients). Alternatively, perhaps we don't care what happens in the southern branch, but we really want the fields traveling past the north monitor to have twice as much overlap with the forward-traveling 3rd eigenmode of that waveguide as the east fields have with their backward-traveling 2nd eigenmode:

f\sub{obj} \equiv \Big( P\sub{3,north} - 2M\sub{2,east}\Big)^2

The point is that the definition of an optimization problem involves not only a set of physical quantities (power fluxes, eigenmode coefficients, etc.) that we compute from meep calculations, but also a rule (the objective function $f$) for crunching those numbers in some specific way to define a single scalar figure of merit.

In mp.adjoint we use the collective term objective quantities for the power fluxes, eigenmode coefficients, and other physical quantities needed to compute the objective function. Similarly, the special geometric subregions of meep geometries with which objective quantities are associated---the cross-sectional flux planes of DFTFlux cells or field-energy boxes of DFTField cells----are known as objective regions.

The [Example Gallery][ExampleGallery.md] includes a worked example of a full successful iterative optimization in which mp.adjoint begins with the design shown above and thoroughly rejiggers it over the course of 50 iterations to yield a device that efficiently routs power around a 90°ree; bend from the eigenmode source (cyan line above) to the 'north' output port.

The asymmetric splitter¶

A splitter seeks to divide incoming power from one source in some specific way among two or more destinations., We will consider an asymmetric splitter in which power arriving from a single incoming waveguide is to be routed into two outgoing waveguides by varying the design of the central coupler region:  Common elements of optimization geometries: Objective regions, objective functions, design regions, basis sets¶

The examples above, distinct though they all are, illustrate the common defining features that are present in every meep optimization problem:

• Objective regions: One or more regions over which to tabulate frequency-domain fields (DFT cells) for use in computing power fluxes, mode-expansion coefficients, and other frequency-domain quantities used in characterizing device performance. Because these regions are used to evaluate objective functions, we refer to them as objective regions. Objective regions may or may not have zero thickness In the examples above, it happens that all objective regions are one-dimensional (zero-thickness) flux monitors, indicated by magenta lines; in a 3D geometry they would be two-dimensional flux planes, still of zero thickness in the normal direction. However, objective regions may also be of nonzero thickness, as for instance if the objective function involves the field energy in a box-shaped subregion of a geometry.

• Objective quantities and the objective function: A specification of which quantities (power fluxes, mode coefficients, energies, etc.) are to be computed for each objective region, and how those quantities are to be crunched mathematically to yield a single number measuring device performance. We refer to the individual quantities as objective quantities, while the overall function that inputs one more more objective quantities and outputs a single numerical score is the objective function.

• Design region: A specification of the region over which the material design is to be optimized, i.e. the region in which the permittivity is given by the design quantity $\epsilon\sup{des}(\mathbf x)$. We refer to this as the design region $\mathcal{V}\sup{des}$.

• Basis: Because the design variable $\epsilon\sup{des}(\mathbf x)$ is a continuous function defined throughout a finite volume of space, technically it involves infinitely many degrees of freedom. To yield a finite-dimensional optimization problem, it is convenient to approximate $\epsilon\sup{des}$ as a finite expansion in some convenient set of basis functions, i.e. $$\epsilon(\mathbf x) \equiv \sum_{d=1}^N \beta_d \mathcal{b}_d(\mathbf x), \qquad \mathbf x\in \mathcal{V}\sup{des},$$ where $\{\mathcal{b}_n(\mathbf x)\}$ is a set of $D$ scalar-valued basis functions defined for $\mathbf x\in\mathcal{V}\sup{des}$. The task of the optimizer then becomes to determine numerical values for the $N$-vector of coefficients $\boldsymbol{\beta}=\{\beta_n\},n=1,\cdots,N.$

For adjoint optimization in meep, the basis set is chosen by the user, either from among a predefined collection of common basis sets, or as an arbitrary user-defined basis set specified by subclassing an abstract base class in mp.adjoint.

Mechanics of meep design optimization¶

With all that by way of background, here's a quick rundown of the process you'll follow to optimize a geometry in meep.adjoint.

1. You write a python script that implements a subclass of OptimizationProblem (an abstract base class in meep.adjoint) to describe your specific problem. In particular, your class must override the following two pure virtual methods in OptimizationProblem:
init_problem: One-time initialization

Inputs an args structure describing command-line options and returns a 5-tuple

   fstr, objective_regions, extra_regions, design_region, basis

defining your objective function, the objective regions on which its inputs (the objective variables) are defined, the design region, and an expansion basis.

create_sim: Instantiation of design-dependent geometries

Inputs a vector of expansion coefficients beta_vector and returns a meep.simulation describing a geometry with the corresponding spatially-varying permittivity.

1. You run computations on your geometry either by executing your script from the shell with command-line options:
  % python HoleyWaveguide.py --beta 0 2.3 --eval_gradient


or equivalently from a python script or console by calling its run() method:

  from HoleyWaveguide import HoleyWaveguide

HW.run()


The actual calculations that may be run in this way range from a single non-iterative computation of the objective function and (optionally) its gradient at a given set of design-variable values, to full-blown iterative design optimization.

Here, in their entirety, are the python scripts implementing the 4 examples described above. (These may also be found in the python/examples/adjoint_optimization subdirectory of your meep installation.)

HoleyWaveguide.py
import sys
import argparse
import numpy as np
import meep as mp

xHat, yHat, zHat, origin, FluxLine,
ParameterizedDielectric, FourierLegendreBasis)

##################################################
##################################################
##################################################
class HoleyWaveguide(OptimizationProblem):

##################################################
##################################################
##################################################

# set problem-specific defaults for existing (general) arguments
parser.set_defaults(fcen=0.5)
parser.set_defaults(df=0.2)
parser.set_defaults(dpml=1.0)

##################################################
##################################################
##################################################
def init_problem(self, args):

#----------------------------------------
# size of computational cell
#----------------------------------------
lcen       = 1.0/args.fcen
dpml       = 0.5*lcen if args.dpml==-1.0 else args.dpml
dair       = 0.5*args.w_wvg if args.dair==-1.0 else args.dair
L          = 3.0*lcen
Lmin       = 6.0*dpml + 2.0*args.r_disc
L          = max(L,Lmin)
sx         = dpml+L+dpml
sy         = dpml+dair+args.w_wvg+dair+dpml
cell_size  = mp.Vector3(sx,sy)

#----------------------------------------
#- design region
#----------------------------------------
design_center = origin
design_size   = mp.Vector3(2.0*args.r_disc, 2.0*args.r_disc)
design_region = mp.Volume(center=design_center, size=design_size)

#----------------------------------------
#- objective regions
#----------------------------------------
x0_east       =  args.r_disc + dpml
x0_west       = -args.r_disc - dpml
y0            = 0.0
flux_length   = 2.0*args.w_wvg
east          = FluxLine(x0_east,y0,flux_length,mp.X,'east')
west          = FluxLine(x0_west,y0,flux_length,mp.X,'west')

objective_regions  = [east, west]

#----------------------------------------
#- optional extra regions for visualization
#----------------------------------------
extra_regions      = [mp.Volume(center=origin, size=cell_size)] if args.full_dfts else []

#----------------------------------------
# basis set
#----------------------------------------

#----------------------------------------
#- source location
#----------------------------------------
source_center    = (x0_west - dpml)*xHat
source_size      = flux_length*yHat

#----------------------------------------
#- objective function
#----------------------------------------
fstr='Abs(P1_east)**2+0.0*(P2_east+P1_west+P2_west+M1_east+M2_east+M1_west+M2_west+S_east+S_west)'

#----------------------------------------
#- internal storage for variables needed later
#----------------------------------------
self.args            = args
self.dpml            = dpml
self.cell_size       = cell_size
self.basis           = basis
self.design_center   = design_center
self.source_center   = source_center
self.source_size     = source_size

return fstr, objective_regions, extra_regions, design_region, basis

##############################################################
##############################################################
##############################################################
def create_sim(self, beta_vector, vacuum=False):

args=self.args
sx=self.cell_size.x

wvg=mp.Block(center=origin, material=mp.Medium(epsilon=args.eps_wvg),
size=mp.Vector3(self.cell_size.x,args.w_wvg))
epsilon_func=ParameterizedDielectric(self.design_center,
self.basis,
beta_vector))

geometry=[wvg] if vacuum else [wvg, disc]

envelope = mp.GaussianSource(args.fcen,fwidth=args.df)
amp=1.0
if callable(getattr(envelope, "fourier_transform", None)):
amp /= envelope.fourier_transform(args.fcen)
sources=[mp.EigenModeSource(src=envelope,
center=self.source_center,
size=self.source_size,
eig_band=self.args.source_mode,
amplitude=amp
)
]

sim=mp.Simulation(resolution=args.res, cell_size=self.cell_size,
boundary_layers=[mp.PML(args.dpml)], geometry=geometry,
sources=sources)

if args.complex_fields:
sim.force_complex_fields=True

return sim

######################################################################
# if executed as a script, we look at our own filename to figure out
# the name of the class above, create an instance of this class called
# opt_prob, and call its run() method.
######################################################################
if __name__ == '__main__':
opt_prob=globals()[__file__.split('/')[-1].split('.')]()
opt_prob.run()


HoleCloak.py
import sys
import argparse
import numpy as np
import meep as mp

xHat, yHat, zHat, origin, FluxLine,
ParameterizedDielectric, FourierLegendreBasis)

##################################################
##################################################
##################################################
class HoleCloak(OptimizationProblem):

##################################################
##################################################
##################################################

parser.add_argument('--eps_disc',    type=float, default=1.0,  help='permittivity in hole region (0.0 for PEC)')

# set problem-specific defaults for existing (general) arguments
parser.set_defaults(fcen=0.5)
parser.set_defaults(df=0.2)
parser.set_defaults(dpml=1.0)

##################################################
##################################################
##################################################
def init_problem(self, args):

#----------------------------------------
# size of computational cell
#----------------------------------------
lcen       = 1.0/args.fcen
dpml       = 0.5*lcen if args.dpml==-1.0 else args.dpml
dair       = 0.5*args.w_wvg if args.dair==-1.0 else args.dair
L          = 3.0*lcen
Lmin       = 6.0*dpml + 2.0*args.r_cloak
L          = max(L,Lmin)
sx         = dpml+L+dpml
sy         = dpml+dair+args.w_wvg+dair+dpml
cell_size  = mp.Vector3(sx, sy, 0.0)

#----------------------------------------
#- design region
#----------------------------------------
design_center = origin
design_size   = mp.Vector3(2.0*args.r_cloak, 2.0*args.r_cloak)
design_region = mp.Volume(center=design_center, size=design_size)

#----------------------------------------
#- objective regions
#----------------------------------------
fluxW_center  =  (+args.r_cloak+ dpml)*xHat
fluxE_center  =  (-args.r_cloak- dpml)*xHat
flux_size     =  2.0*args.w_wvg*yHat

#fluxW_region  = mp.FluxRegion(center=fluxW_center, size=flux_size, direction=mp.X)
#fluxE_region  = mp.FluxRegion(center=fluxE_center, size=flux_size, direction=mp.X)
x0_east       =  args.r_cloak + dpml
x0_west       = -args.r_cloak - dpml
y0            = 0.0
flux_length   = 2.0*args.w_wvg
east          = FluxLine(x0_east,y0,flux_length,mp.X,'east')
west          = FluxLine(x0_west,y0,flux_length,mp.X,'west')

objective_regions  = [east, west]

#----------------------------------------
#- optional extra regions for visualization
#----------------------------------------
extra_regions      = [mp.Volume(center=origin, size=cell_size)] if args.full_dfts else []

#----------------------------------------
# basis set
#----------------------------------------
nr_max=args.nr_max, kphi_max=args.kphi_max)

#----------------------------------------
#- source location
#----------------------------------------
source_center    = (x0_west-dpml)*xHat
source_size      = flux_length*yHat

#----------------------------------------
#- objective function
#----------------------------------------
fstr='Abs(P1_east)**2+0.0*(P1_west + M1_east + M1_west + S_west + S_east)'

#----------------------------------------
#- internal storage for variables needed later
#----------------------------------------
self.args            = args
self.dpml            = dpml
self.cell_size       = cell_size
self.basis           = basis
self.design_center   = design_center
self.source_center   = source_center
self.source_size     = source_size

return fstr, objective_regions, extra_regions, design_region, basis

##############################################################
##############################################################
##############################################################
def create_sim(self, beta_vector, vacuum=False):

args=self.args
sx=self.cell_size.x

wvg=mp.Block(center=origin, material=mp.Medium(epsilon=args.eps_wvg),
size=mp.Vector3(self.cell_size.x,args.w_wvg))
epsilon_func=ParameterizedDielectric(self.design_center,
self.basis,
beta_vector))
material=(mp.metal if args.eps_disc==0 else
mp.Medium(epsilon=args.eps_disc)))

geometry=[wvg] if vacuum else [wvg, cloak, disc]

envelope = mp.GaussianSource(args.fcen,fwidth=args.df)
amp=1.0
if callable(getattr(envelope, "fourier_transform", None)):
amp /= envelope.fourier_transform(args.fcen)
sources=[mp.EigenModeSource(src=envelope,
center=self.source_center,
size=self.source_size,
eig_band=self.args.source_mode,
amplitude=amp
)
]

sim=mp.Simulation(resolution=args.res, cell_size=self.cell_size,
boundary_layers=[mp.PML(args.dpml)], geometry=geometry,
sources=sources)

if args.complex_fields:
sim.force_complex_fields=True

return sim

######################################################################
# if executed as a script, we look at our own filename to figure out
# the name of the class above, create an instance of this class called
# opt_prob, and call its run() method.
######################################################################
if __name__ == '__main__':
opt_prob=globals()[__file__.split('/')[-1].split('.')]()
opt_prob.run()


CrossRouter.py
import numpy as np
import meep as mp

xHat, yHat, zHat, origin,
ParameterizedDielectric, FiniteElementBasis)

##################################################
##################################################
##################################################
class CrossRouter(OptimizationProblem):

##################################################
##################################################
##################################################

parser.add_argument('--wh',       type=float, default=1.5,  help='width of horizontal waveguide')
parser.add_argument('--wv',       type=float, default=1.5,  help='width of vertical waveguide')
parser.add_argument('--l_stub',   type=float, default=3.0,  help='waveguide input/output stub length')
parser.add_argument('--l_design', type=float, default=4.0,  help='design region side length')
parser.add_argument('--nfe',      type=int,   default=2,    help='number of finite elements per unit length')

# set problem-specific defaults for existing (general) arguments
parser.set_defaults(fcen=0.5)
parser.set_defaults(df=0.2)
parser.set_defaults(dpml=1.0)
parser.set_defaults(epsilon_design=6.0)

##################################################
##################################################
##################################################
def init_problem(self, args):

#----------------------------------------
# size of computational cell
#----------------------------------------
lcen          = 1.0/args.fcen
dpml          = 0.5*lcen if args.dpml == -1.0 else args.dpml
design_length = 2.0*args.r_design if args.r_design > 0.0 else args.l_design
sx = sy       = dpml + args.l_stub + design_length + args.l_stub + dpml
cell_size     = mp.Vector3(sx, sy, 0.0)

#----------------------------------------
#- design region bounding box
#----------------------------------------
design_center = origin
design_size   = mp.Vector3(design_length, design_length)
design_region = mp.Volume(center=design_center, size=design_size)

#----------------------------------------
#- objective and source regions
#----------------------------------------
gap            =  args.l_stub/6.0                    # gap between source region and flux monitor
d_flux         =  0.5*(design_length + args.l_stub)  # distance from origin to NSEW flux monitors
d_source       =  d_flux + gap                       # distance from origin to source
d_flx2         =  d_flux + 2.0*gap
l_flux_NS      =  2.0*args.wv
l_flux_EW      =  2.0*args.wh
north          =  FluxLine(0.0, +d_flux, l_flux_NS, mp.Y, 'north')
south          =  FluxLine(0.0, -d_flux, l_flux_NS, mp.Y, 'south')
east           =  FluxLine(+d_flux, 0.0, l_flux_EW, mp.X, 'east')
west1          =  FluxLine(-d_flux, 0.0, l_flux_EW, mp.X, 'west1')
west2          =  FluxLine(-d_flx2, 0.0, l_flux_EW, mp.X, 'west2')

objective_regions  = [north, south, east, west1, west2]

source_center  =  mp.Vector3(-d_source, 0.0)
source_size    =  mp.Vector3(0.0,l_flux_EW)

#----------------------------------------
#- optional extra regions for visualization
#----------------------------------------
extra_regions  = [mp.Volume(center=origin, size=cell_size)] if args.full_dfts else []

#----------------------------------------
# basis set
#----------------------------------------
basis = FiniteElementBasis(lx=args.l_design, ly=args.l_design, density=args.nfe)

#----------------------------------------
#- objective function
#----------------------------------------
fstr=(   '   {:s}*Abs(P1_north)**2'.format('0.0' if args.n_weight==0.0 else '{}'.format(args.n_weight))
+ ' + {:s}*Abs(M1_south)**2'.format('0.0' if args.s_weight==0.0 else '{}'.format(args.s_weight))
+ ' + {:s}*Abs(P1_east)**2'.format('0.0'  if args.e_weight==0.0 else '{}'.format(args.e_weight))
+ ' + 0.0*(P1_north + M1_south + P1_east + P1_west1 + P1_west2)'
+ ' + 0.0*(M1_north + M1_south + M1_east + M1_west1 + M1_west2)'
+ ' + 0.0*(S_north + S_south + S_east + S_west1 + S_west2)'
)

#----------------------------------------
#- internal storage for variables needed later
#----------------------------------------
self.args            = args
self.dpml            = dpml
self.cell_size       = cell_size
self.basis           = basis
self.design_center   = design_center
self.design_size     = design_size
self.source_center   = source_center
self.source_size     = source_size

if args.eps_design is None:
args.eps_design = args.eps

return fstr, objective_regions, extra_regions, design_region, basis

##############################################################
##############################################################
##############################################################
def create_sim(self, beta_vector, vacuum=False):

args=self.args

hwvg=mp.Block(center=origin, material=mp.Medium(epsilon=args.eps),
size=mp.Vector3(self.cell_size.x,args.wh))
vwvg=mp.Block(center=origin, material=mp.Medium(epsilon=args.eps),
size=mp.Vector3(args.wv,self.cell_size.y))

if args.r_design>0.0:
epsilon_func=ParameterizedDielectric(self.design_center,
self.basis,
beta_vector))
else:
router=mp.Block(center=self.design_center, size=self.design_size,
epsilon_func=ParameterizedDielectric(self.design_center,
self.basis,
beta_vector))
geometry=[hwvg, vwvg, router]

envelope = mp.GaussianSource(args.fcen,fwidth=args.df)
amp=1.0
if callable(getattr(envelope, "fourier_transform", None)):
amp /= envelope.fourier_transform(args.fcen)
sources=[mp.EigenModeSource(src=envelope,
center=self.source_center,
size=self.source_size,
eig_band=args.source_mode,
amplitude=amp
)
]

sim=mp.Simulation(resolution=args.res, cell_size=self.cell_size,
boundary_layers=[mp.PML(self.dpml)], geometry=geometry,
sources=sources)

if args.complex_fields:
sim.force_complex_fields=True

return sim

######################################################################
# if executed as a script, we look at our own filename to figure out
# the name of the class above, create an instance of this class called
# op, and call its run() method.
######################################################################
if __name__ == '__main__':
op=globals()[__file__.split('/')[-1].split('.')]()
op.run()


AsymmetricSplitter.py
import sys
import argparse
import numpy as np
import meep as mp

xHat, yHat, zHat, origin, FluxLine,
ParameterizedDielectric, FiniteElementBasis)

##################################################
##################################################
##################################################
class Splitter12(OptimizationProblem):

##################################################
##################################################
##################################################

parser.add_argument('--w_in',        type=float, default=1.0,  help='width of input waveguide')
parser.add_argument('--w_out1',      type=float, default=0.5,  help='width of output waveguide 1')
parser.add_argument('--w_out2',      type=float, default=0.5,  help='width of output waveguide 2')
parser.add_argument('--l_stub',      type=float, default=3.0,  help='length of waveguide input/output stub')
parser.add_argument('--l_design',    type=float, default=2.0,  help='length of design region')
parser.add_argument('--h_design',    type=float, default=6.0,  help='height of design region')
parser.add_argument('--eps_in',      type=float, default=6.0,  help='input waveguide permittivity')
parser.add_argument('--eps_out1',    type=float, default=2.0,  help='output waveguide 1 permittivity')
parser.add_argument('--eps_out2',    type=float, default=12.0, help='output waveguide 2 permittivity')
parser.add_argument('--nfe',         type=int,   default=2,    help='number of finite elements per unit length')

# set problem-specific defaults for existing (general) arguments
parser.set_defaults(fcen=0.5)
parser.set_defaults(df=0.2)
parser.set_defaults(dpml=1.0)

##################################################
##################################################
##################################################
def init_problem(self, args):

#----------------------------------------
# size of computational cell
#----------------------------------------
lcen       = 1.0/args.fcen
dpml       = 0.5*lcen if args.dpml==-1.0 else args.dpml
dair       = 0.5*args.w_in if args.dair==-1.0 else args.dair
sx         = dpml + args.l_stub + args.l_design + args.l_stub + dpml
sy         = dpml + dair + args.h_design + dair + dpml
cell_size  = mp.Vector3(sx, sy, 0.0)

#----------------------------------------
#- design region
#----------------------------------------
design_center = origin
design_size   = mp.Vector3(args.l_design, args.h_design, 0.0)
design_region = mp.Volume(center=design_center, size=design_size)

#----------------------------------------
#- objective regions
#----------------------------------------
x_in          =  -0.5*(args.l_design + args.l_stub)
x_out         =  +0.5*(args.l_design + args.l_stub)
y_out1        =  +0.25*args.h_design
y_out2        =  -0.25*args.h_design

flux_in       =  FluxLine(x_in,     0.0, 2.0*args.w_in,   mp.X, 'in')
flux_out1     =  FluxLine(x_out, y_out1, 2.0*args.w_out1, mp.X, 'out1')
flux_out2     =  FluxLine(x_out, y_out2, 2.0*args.w_out2, mp.X, 'out2')

objective_regions  = [flux_in, flux_out1, flux_out2]

#----------------------------------------
#- optional extra regions for visualization if the --full-dfts options is present.
#----------------------------------------
extra_regions      = [mp.Volume(center=origin, size=cell_size)] if args.full_dfts else []

#----------------------------------------
# forward source region
#----------------------------------------
source_center    =  (x_in - 0.25*args.l_stub)*xHat
source_size      =  2.0*args.w_in*yHat

#----------------------------------------
# basis set
#----------------------------------------
basis = FiniteElementBasis(args.l_design, args.h_design, args.nfe)

#----------------------------------------
#- objective function
#----------------------------------------
fstr = (   'Abs(P1_out1)**2'
+ '+0.0*(P1_out1 + M1_out1)'
+ '+0.0*(P1_out2 + M1_out2)'
+ '+0.0*(P1_in   + M1_in + S_out1 + S_out2 + S_in)'
)

#----------------------------------------
#- internal storage for variables needed later
#----------------------------------------
self.args            = args
self.dpml            = dpml
self.cell_size       = cell_size
self.basis           = basis
self.design_center   = origin
self.design_size     = design_size
self.source_center   = source_center
self.source_size     = source_size

return fstr, objective_regions, extra_regions, design_region, basis

##############################################################
##############################################################
##############################################################
def create_sim(self, beta_vector, vacuum=False):

args=self.args
sx=self.cell_size.x

x_in   = -0.5*(args.l_design + args.l_stub)
x_out  = +0.5*(args.l_design + args.l_stub)
y_out1 = +0.25*args.h_design
y_out2 = -0.25*args.h_design

wvg_in = mp.Block( center=mp.Vector3(x_in,0.0),
size=mp.Vector3(args.l_stub,args.w_in),
material=mp.Medium(epsilon=args.eps_in))
wvg_out1 = mp.Block( center=mp.Vector3(x_out,y_out1),
size=mp.Vector3(args.l_stub,args.w_out1),
material=mp.Medium(epsilon=args.eps_out1))
wvg_out2 = mp.Block( center=mp.Vector3(x_out,y_out2),
size=mp.Vector3(args.l_stub,args.w_out2),
material=mp.Medium(epsilon=args.eps_out2))
design   = mp.Block( center=origin,
size=mp.Vector3(args.l_design,args.h_design),
epsilon_func=ParameterizedDielectric(self.design_center,
self.basis,
beta_vector)
)

geometry=[wvg_in, wvg_out1, wvg_out2, design]

envelope = mp.GaussianSource(args.fcen,fwidth=args.df)
amp=1.0
if callable(getattr(envelope, "fourier_transform", None)):
amp /= envelope.fourier_transform(args.fcen)
sources=[mp.EigenModeSource(src=envelope,
center=self.source_center,
size=self.source_size,
eig_band=self.args.source_mode,
amplitude=amp
)
]

sim=mp.Simulation(resolution=args.res, cell_size=self.cell_size,
boundary_layers=[mp.PML(args.dpml)], geometry=geometry,
sources=sources)

if args.complex_fields:
sim.force_complex_fields=True

return sim

######################################################################
# if executed as a script, we look at our own filename to figure out
# the name of the class above, create an instance of this class called
# opt_prob, and call its run() method.
######################################################################
if __name__ == '__main__':
opt_prob=globals()[__file__.split('/')[-1].split('.')]()
opt_prob.run()