diff --git a/python/adjoint/basis.py b/python/adjoint/basis.py index 3e138ee42..ac28d512c 100644 --- a/python/adjoint/basis.py +++ b/python/adjoint/basis.py @@ -50,7 +50,7 @@ def set_rho_vector(self, rho_vector): class BilinearInterpolationBasis(Basis): - ''' + ''' Simple bilinear interpolation basis set. ''' def __init__(self, resolution, symmetry=None, **kwargs): @@ -65,26 +65,26 @@ def __init__(self, resolution, symmetry=None, **kwargs): self.symmetry = symmetry if mp.X in set(self.symmetry): - self.Nx = int(resolution * self.volume.size.x / 2) + self.Nx = int(resolution * self.volume.size.x / 2) + 1 self.rho_x = np.linspace( self.volume.center.x, self.volume.center.x + self.volume.size.x / 2, self.Nx) self.mirror_X = True else: - self.Nx = int(resolution * self.volume.size.x) + self.Nx = int(resolution * self.volume.size.x) + 1 self.rho_x = np.linspace( self.volume.center.x - self.volume.size.x / 2, self.volume.center.x + self.volume.size.x / 2, self.Nx) self.mirror_X = False if mp.Y in set(self.symmetry): - self.Ny = int(resolution * self.volume.size.y / 2) + self.Ny = int(resolution * self.volume.size.y / 2) + 1 self.rho_y = np.linspace( self.volume.center.y, self.volume.center.y + self.volume.size.y / 2, self.Ny) self.mirror_Y = True else: - self.Ny = int(resolution * self.volume.size.y) + self.Ny = int(resolution * self.volume.size.y) + 1 self.rho_y = np.linspace( self.volume.center.y - self.volume.size.y / 2, self.volume.center.y + self.volume.size.y / 2, self.Ny) @@ -204,7 +204,7 @@ def gen_interpolation_matrix( ): ''' Generates a bilinear interpolation matrix. - + Arguments: rho_x ................ [N,] numpy array - original x array mapping to povided data rho_y ................ [N,] numpy array - original y array mapping to povided data diff --git a/python/adjoint/filters.py b/python/adjoint/filters.py index b2605f740..b84e218a5 100644 --- a/python/adjoint/filters.py +++ b/python/adjoint/filters.py @@ -50,16 +50,16 @@ def _edge_pad(arr, pad): def _zero_pad(arr, pad): # fill sides - left = npa.tile(0, (pad[0][0], arr.shape[1])) # left side + left = npa.tile(0, (pad[0][0], arr.shape[1])) # left side right = npa.tile(0, (pad[0][1], arr.shape[1])) # right side - top = npa.tile(0, (arr.shape[0], pad[1][0])) # top side - bottom = npa.tile(0, (arr.shape[0], pad[1][1])) # bottom side + top = npa.tile(0, (arr.shape[0], pad[1][0])) # top side + bottom = npa.tile(0, (arr.shape[0], pad[1][1])) # bottom side # fill corners - top_left = npa.tile(0, (pad[0][0], pad[1][0])) # top left - top_right = npa.tile(0, (pad[0][1], pad[1][0])) # top right + top_left = npa.tile(0, (pad[0][0], pad[1][0])) # top left + top_right = npa.tile(0, (pad[0][1], pad[1][0])) # top right bottom_left = npa.tile(0, (pad[0][0], pad[1][1])) # bottom left - bottom_right = npa.tile(0, (pad[0][1], pad[1][1])) # bottom right + bottom_right = npa.tile(0, (pad[0][1], pad[1][1])) # bottom right out = npa.concatenate((npa.concatenate( (top_left, top, top_right)), npa.concatenate((left, arr, right)), @@ -74,7 +74,7 @@ def simple_2d_filter(x, kernel, Lx, Ly, resolution, symmetries=[]): """A simple 2d filter algorithm that is differentiable with autograd. Uses a 2D fft approach since it is typically faster and preserves the shape of the input and output arrays. - + The ffts pad the operation to prevent any circular convolution garbage. Parameters @@ -91,15 +91,15 @@ def simple_2d_filter(x, kernel, Lx, Ly, resolution, symmetries=[]): Resolution of the design grid (not the meep simulation resolution) symmetries : list Symmetries to impose on the parameter field (either mp.X or mp.Y) - + Returns ------- array_like (2D) The output of the 2d convolution. """ # Get 2d parameter space shape - Nx = int(Lx * resolution) - Ny = int(Ly * resolution) + Nx = int(Lx * resolution) + 1 + Ny = int(Ly * resolution) + 1 (kx, ky) = kernel.shape # Adjust parameter space shape for symmetries @@ -151,8 +151,8 @@ def simple_2d_filter(x, kernel, Lx, Ly, resolution, symmetries=[]): def cylindrical_filter(x, radius, Lx, Ly, resolution, symmetries=[]): - '''A uniform cylindrical filter [1]. Typically allows for sharper transitions. - + '''A uniform cylindrical filter [1]. Typically allows for sharper transitions. + Parameters ---------- x : array_like (2D) @@ -172,15 +172,15 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution, symmetries=[]): ------- array_like (2D) Filtered design parameters. - + References ---------- - [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. ''' # Get 2d parameter space shape - Nx = int(Lx * resolution) - Ny = int(Ly * resolution) + Nx = int(Lx * resolution) + 1 + Ny = int(Ly * resolution) + 1 # Formulate grid over entire design region xv, yv = np.meshgrid(np.linspace(-Lx / 2, Lx / 2, Nx), @@ -202,7 +202,7 @@ def cylindrical_filter(x, radius, Lx, Ly, resolution, symmetries=[]): def conic_filter(x, radius, Lx, Ly, resolution, symmetries=[]): '''A linear conic filter, also known as a "Hat" filter in the literature [1]. - + Parameters ---------- x : array_like (2D) @@ -222,15 +222,15 @@ def conic_filter(x, radius, Lx, Ly, resolution, symmetries=[]): ------- array_like (2D) Filtered design parameters. - + References ---------- - [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. ''' # Get 2d parameter space shape - Nx = int(Lx * resolution) - Ny = int(Ly * resolution) + Nx = int(Lx * resolution) + 1 + Ny = int(Ly * resolution) + 1 # Formulate grid over entire design region xv, yv = np.meshgrid(np.linspace(-Lx / 2, Lx / 2, Nx), @@ -254,7 +254,7 @@ def conic_filter(x, radius, Lx, Ly, resolution, symmetries=[]): def gaussian_filter(x, sigma, Lx, Ly, resolution, symmetries=[]): '''A simple gaussian filter of the form exp(-x **2 / sigma ** 2) [1]. - + Parameters ---------- x : array_like (2D) @@ -272,15 +272,15 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution, symmetries=[]): ------- array_like (2D) Filtered design parameters. - + References ---------- - [1] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of + [1] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. ''' # Get 2d parameter space shape - Nx = int(Lx * resolution) - Ny = int(Ly * resolution) + Nx = int(Lx * resolution) + 1 + Ny = int(Ly * resolution) + 1 gaussian = lambda x, sigma: np.exp(-x**2 / sigma**2) @@ -309,7 +309,7 @@ def gaussian_filter(x, sigma, Lx, Ly, resolution, symmetries=[]): def exponential_erosion(x, radius, beta, Lx, Ly, resolution): ''' Performs and exponential erosion operation. - + Parameters ---------- x : array_like @@ -329,13 +329,13 @@ def exponential_erosion(x, radius, beta, Lx, Ly, resolution): ------- array_like Eroded design parameters. - + References ---------- - [1] Sigmund, O. (2007). Morphology-based black and white filters for topology optimization. + [1] Sigmund, O. (2007). Morphology-based black and white filters for topology optimization. Structural and Multidisciplinary Optimization, 33(4-5), 401-424. - [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of - morphological close-open and open-close filters for topology optimization. Structural + [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of + morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. ''' @@ -346,7 +346,7 @@ def exponential_erosion(x, radius, beta, Lx, Ly, resolution): def exponential_dilation(x, radius, beta, Lx, Ly, resolution): ''' Performs a exponential dilation operation. - + Parameters ---------- x : array_like @@ -366,13 +366,13 @@ def exponential_dilation(x, radius, beta, Lx, Ly, resolution): ------- array_like Dilated design parameters. - + References ---------- - [1] Sigmund, O. (2007). Morphology-based black and white filters for topology optimization. + [1] Sigmund, O. (2007). Morphology-based black and white filters for topology optimization. Structural and Multidisciplinary Optimization, 33(4-5), 401-424. - [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of - morphological close-open and open-close filters for topology optimization. Structural + [2] Schevenels, M., & Sigmund, O. (2016). On the implementation and effectiveness of + morphological close-open and open-close filters for topology optimization. Structural and Multidisciplinary Optimization, 54(1), 15-21. ''' @@ -383,7 +383,7 @@ def exponential_dilation(x, radius, beta, Lx, Ly, resolution): def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): ''' Performs a heaviside erosion operation. - + Parameters ---------- x : array_like @@ -403,11 +403,11 @@ def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): ------- array_like Eroded design parameters. - + References ---------- - [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology - optimization using nodal design variables and projection functions. International journal for + [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology + optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. ''' @@ -417,7 +417,7 @@ def heaviside_erosion(x, radius, beta, Lx, Ly, resolution): def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): ''' Performs a heaviside dilation operation. - + Parameters ---------- x : array_like @@ -437,11 +437,11 @@ def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): ------- array_like Dilated design parameters. - + References ---------- - [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology - optimization using nodal design variables and projection functions. International journal for + [1] Guest, J. K., Prévost, J. H., & Belytschko, T. (2004). Achieving minimum length scale in topology + optimization using nodal design variables and projection functions. International journal for numerical methods in engineering, 61(2), 238-254. ''' @@ -451,7 +451,7 @@ def heaviside_dilation(x, radius, beta, Lx, Ly, resolution): def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): ''' Performs a geometric erosion operation. - + Parameters ---------- x : array_like @@ -471,10 +471,10 @@ def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): ------- array_like Eroded design parameters. - + References ---------- - [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the + [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. ''' x_hat = npa.log(x + alpha) @@ -484,7 +484,7 @@ def geometric_erosion(x, radius, alpha, Lx, Ly, resolution): def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): ''' Performs a geometric dilation operation. - + Parameters ---------- x : array_like @@ -504,10 +504,10 @@ def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): ------- array_like Dilated design parameters. - + References ---------- - [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the + [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. ''' @@ -518,7 +518,7 @@ def geometric_dilation(x, radius, alpha, Lx, Ly, resolution): def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): ''' Performs a harmonic erosion operation. - + Parameters ---------- x : array_like @@ -538,10 +538,10 @@ def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): ------- array_like Eroded design parameters. - + References ---------- - [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the + [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. ''' @@ -552,7 +552,7 @@ def harmonic_erosion(x, radius, alpha, Lx, Ly, resolution): def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): ''' Performs a harmonic dilation operation. - + Parameters ---------- x : array_like @@ -572,10 +572,10 @@ def harmonic_dilation(x, radius, alpha, Lx, Ly, resolution): ------- array_like Dilated design parameters. - + References ---------- - [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the + [1] Svanberg, K., & Svärd, H. (2013). Density filters for topology optimization based on the Pythagorean means. Structural and Multidisciplinary Optimization, 48(5), 859-875. ''' @@ -601,7 +601,7 @@ def tanh_projection(x, beta, eta): beta : float Thresholding parameter (0 to infinity). Dictates how "binary" the output will be. eta: float - Threshold point (0 to 1) + Threshold point (0 to 1) Returns ------- @@ -609,7 +609,7 @@ def tanh_projection(x, beta, eta): Projected and flattened design parameters. References ---------- - [1] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and robust + [1] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767-784. ''' @@ -629,16 +629,16 @@ def heaviside_projection(x, beta, eta): beta : float Thresholding parameter (0 to infinity). Dictates how "binary" the output will be. eta: float - Threshold point (0 to 1) + Threshold point (0 to 1) Returns ------- array_like Projected and flattened design parameters. - + References ---------- - [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. ''' @@ -658,24 +658,24 @@ def get_threshold_wang(delta, sigma): '''Calculates the threshold point according to the gaussian filter radius (`sigma`) and the perturbation parameter (`sigma`) needed to ensure the proper length scale and morphological transformation according to Wang et. al. [2]. - + Parameters ---------- sigma : float Smoothing radius (in meep units) delta : float Perturbation parameter (in meep units) - + Returns ------- float Threshold point (`eta`) - + References ---------- - [1] Wang, F., Jensen, J. S., & Sigmund, O. (2011). Robust topology optimization of + [1] Wang, F., Jensen, J. S., & Sigmund, O. (2011). Robust topology optimization of photonic crystal waveguides with tailored dispersion properties. JOSA B, 28(3), 387-397. - [2] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of + [2] Wang, E. W., Sell, D., Phan, T., & Fan, J. A. (2019). Robust design of topology-optimized metasurfaces. Optical Materials Express, 9(2), 469-482. ''' @@ -685,32 +685,32 @@ def get_threshold_wang(delta, sigma): def get_eta_from_conic(b, R): ''' Extracts the eroded threshold point (`eta_e`) for a conic filter given the desired minimum length (`b`) and the filter radius (`R`). This only works for conic filters. - + Note that the units for `b` and `R` can be arbitrary so long as they are consistent. - + Results in paper were thresholded using a "tanh" Heaviside projection. - + Parameters ---------- b : float Desired minimum length scale. R : float Conic filter radius - + Returns ------- float The eroded threshold point (1-eta) - + References ---------- - [1] Qian, X., & Sigmund, O. (2013). Topological design of electromechanical actuators with - robustness toward over-and under-etching. Computer Methods in Applied + [1] Qian, X., & Sigmund, O. (2013). Topological design of electromechanical actuators with + robustness toward over-and under-etching. Computer Methods in Applied Mechanics and Engineering, 253, 237-251. - [2] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and - robust formulations in topology optimization. Structural and Multidisciplinary + [2] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and + robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767-784. - [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. ''' @@ -729,28 +729,28 @@ def get_eta_from_conic(b, R): def get_conic_radius_from_eta_e(b, eta_e): """Calculates the corresponding filter radius given the minimum length scale (b) and the desired eroded threshold point (eta_e). - + Parameters ---------- b : float Desired minimum length scale. eta_e : float Eroded threshold point (1-eta) - + Returns ------- float Conic filter radius. - + References ---------- - [1] Qian, X., & Sigmund, O. (2013). Topological design of electromechanical actuators with - robustness toward over-and under-etching. Computer Methods in Applied + [1] Qian, X., & Sigmund, O. (2013). Topological design of electromechanical actuators with + robustness toward over-and under-etching. Computer Methods in Applied Mechanics and Engineering, 253, 237-251. - [2] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and - robust formulations in topology optimization. Structural and Multidisciplinary + [2] Wang, F., Lazarov, B. S., & Sigmund, O. (2011). On projection methods, convergence and + robust formulations in topology optimization. Structural and Multidisciplinary Optimization, 43(6), 767-784. - [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [3] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. """ if (eta_e >= 0.5) and (eta_e < 0.75): @@ -764,7 +764,7 @@ def get_conic_radius_from_eta_e(b, eta_e): def indicator_solid(x, c, filter_f, threshold_f, resolution): '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. - + Parameters ---------- x : array_like @@ -777,15 +777,15 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): Filter function. Must be differntiable by autograd. threshold_f : function_handle Threshold function. Must be differntiable by autograd. - + Returns ------- array_like Indicator value - + References ---------- - [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by + [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. ''' @@ -804,7 +804,7 @@ def indicator_solid(x, c, filter_f, threshold_f, resolution): def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): '''Calculates the constraint function of the solid phase needed for minimum length optimization [1]. - + Parameters ---------- x : array_like @@ -817,20 +817,20 @@ def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): Filter function. Must be differntiable by autograd. threshold_f : function_handle Threshold function. Must be differntiable by autograd. - + Returns ------- float Constraint value - + Example ------- >> g_s = constraint_solid(x,c,eta_e,filter_f,threshold_f) # constraint >> g_s_grad = grad(constraint_solid,0)(x,c,eta_e,filter_f,threshold_f) # gradient - + References ---------- - [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by + [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. ''' @@ -842,7 +842,7 @@ def constraint_solid(x, c, eta_e, filter_f, threshold_f, resolution): def indicator_void(x, c, filter_f, threshold_f, resolution): '''Calculates the indicator function for the void phase needed for minimum length optimization [1]. - + Parameters ---------- x : array_like @@ -855,15 +855,15 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): Filter function. Must be differntiable by autograd. threshold_f : function_handle Threshold function. Must be differntiable by autograd. - + Returns ------- array_like Indicator value - + References ---------- - [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by + [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. ''' @@ -881,7 +881,7 @@ def indicator_void(x, c, filter_f, threshold_f, resolution): def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): '''Calculates the constraint function of the void phase needed for minimum length optimization [1]. - + Parameters ---------- x : array_like @@ -894,20 +894,20 @@ def constraint_void(x, c, eta_d, filter_f, threshold_f, resolution): Filter function. Must be differntiable by autograd. threshold_f : function_handle Threshold function. Must be differntiable by autograd. - + Returns ------- float Constraint value - + Example ------- >> g_v = constraint_void(p,c,eta_d,filter_f,threshold_f) # constraint >> g_v_grad = tensor_jacobian_product(constraint_void,0)(p,c,eta_d,filter_f,threshold_f,g_s) # gradient - + References ---------- - [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by + [1] Zhou, M., Lazarov, B. S., Wang, F., & Sigmund, O. (2015). Minimum length scale in topology optimization by geometric constraints. Computer Methods in Applied Mechanics and Engineering, 293, 266-282. ''' @@ -921,20 +921,20 @@ def gray_indicator(x): '''Calculates a measure of "grayness" according to [1]. Lower numbers ( < 2%) indicate a good amount of binarization [1]. - + Parameters ---------- x : array_like Filtered and thresholded design parameters (between 0 and 1) - + Returns ------- float Measure of "grayness" (in percent) - + References ---------- - [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in + [1] Lazarov, B. S., Wang, F., & Sigmund, O. (2016). Length scale and manufacturability in density-based topology optimization. Archive of Applied Mechanics, 86(1-2), 189-218. ''' return npa.mean(4 * x.flatten() * (1 - x.flatten())) * 100 diff --git a/python/tests/test_adjoint_solver.py b/python/tests/test_adjoint_solver.py index e509d78f7..481366cc3 100644 --- a/python/tests/test_adjoint_solver.py +++ b/python/tests/test_adjoint_solver.py @@ -12,7 +12,7 @@ MonitorObject = Enum('MonitorObject', 'EIGENMODE DFT') -resolution = 25 +resolution = 30 silicon = mp.Medium(epsilon=12) @@ -26,8 +26,8 @@ design_region_size = mp.Vector3(1.5,1.5) design_region_resolution = int(2*resolution) -Nx = int(design_region_resolution*design_region_size.x) -Ny = int(design_region_resolution*design_region_size.y) +Nx = int(design_region_resolution*design_region_size.x) + 1 +Ny = int(design_region_resolution*design_region_size.y) + 1 ## ensure reproducible results np.random.seed(9861548) @@ -213,7 +213,7 @@ def test_adjoint_solver_DFT_fields(self): adj_scale = (dp[None,:]@adjsol_grad).flatten() fd_grad = S12_perturbed-S12_unperturbed print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.3 if mp.is_single_precision() else 0.01 + tol = 0.04 if mp.is_single_precision() else 0.005 self.assertClose(adj_scale,fd_grad,epsilon=tol) @@ -231,7 +231,7 @@ def test_adjoint_solver_eigenmode(self): ## compare objective results print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-3) + self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) ## compute perturbed S12 S12_perturbed = forward_simulation(p+dp, MonitorObject.EIGENMODE, frequencies, use_complex, k) @@ -242,7 +242,7 @@ def test_adjoint_solver_eigenmode(self): adj_scale = (dp[None,:]@adjsol_grad).flatten() fd_grad = S12_perturbed-S12_unperturbed print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.15 if mp.is_single_precision() else 0.03 + tol = 0.04 if mp.is_single_precision() else 0.01 self.assertClose(adj_scale,fd_grad,epsilon=tol) def test_gradient_backpropagation(self): @@ -271,8 +271,7 @@ def test_gradient_backpropagation(self): ## compare objective results print("S12 -- adjoint solver: {}, traditional simulation: {}".format(adjsol_obj,S12_unperturbed)) - tol = 1e-2 if mp.is_single_precision() else 1e-3 - self.assertClose(adjsol_obj,S12_unperturbed,epsilon=tol) + self.assertClose(adjsol_obj,S12_unperturbed,epsilon=1e-6) ## compute perturbed S12 S12_perturbed = forward_simulation(mapping(p+dp,filter_radius,eta,beta), MonitorObject.EIGENMODE,frequencies) @@ -282,7 +281,7 @@ def test_gradient_backpropagation(self): adj_scale = (dp[None,:]@bp_adjsol_grad).flatten() fd_grad = S12_perturbed-S12_unperturbed print("Directional derivative -- adjoint solver: {}, FD: {}".format(adj_scale,fd_grad)) - tol = 0.05 if mp.is_single_precision() else 0.03 + tol = 0.02 if mp.is_single_precision() else 0.01 self.assertClose(adj_scale,fd_grad,epsilon=tol)