Interface Problem by Variational Method

Sym v1.3.0

This tutorial demonstrates the process of solving a PDE using the variational formulation. It shows how to use the variational method to solve the interface PDE problem using Modulus Sym. The use of variational method (weak formulation) also allows you to handle problems with point source with ease and this is covered in this tutorial too. In this tutorial you will learn:

  1. How to solve a PDE in its variational form (continuous and discontinuous) in Modulus Sym.

  2. How to generate test functions and their derivative data on desired point sets.

  3. How to use quadrature in the Modulus Sym.

  4. How to solve a problem with a point source (Dirac Delta function).


This tutorial assumes that you have completed tutorial Introductory Example on Lid Driven Cavity and have familiarized yourself with the basics of the Modulus Sym APIs. Also, see Section Weak solution of PDEs using PINNs from the Theory chapter for more details on weak solutions of PDEs.

All the scripts referred in this tutorial can be found in examples/discontinuous_galerkin/.


The Python package quadpy is required for these examples. Install using pip install quadpy (Also refer to Modulus Sym with Docker Image (Recommended)).

This tutorial, demonstrates solving the Poisson equation with Dirichlet boundary conditions. The problem represents an interface between two domains. Let \(\Omega_1 = (0,0.5)\times(0,1)\), \(\Omega_2 = (0.5,1)\times(0,1)\), \(\Omega=(0,1)^2\). The interface is \(\Gamma=\overline{\Omega}_1\cap\overline{\Omega}_2\), and the Dirichlet boundary is \(\Gamma_D=\partial\Omega\). The domain for the problem can be visualized in the Fig. 102. The problem was originally defined in 1.


Fig. 102 Left: Domain of interface problem. Right: True Solution

The PDEs for the problem are defined as

(178)\[-\Delta u = f \quad \mbox{in}\quad \Omega,\]
(179)\[u = g_D \quad \mbox{on} \quad \Gamma_D,\]
(180)\[\left[\frac{\partial u}{\partial \mathbf{n}} \right] =g_I \quad \mbox{on} \quad\Gamma,\]

where \(f=-2\), \(g_I=2\) and

(181)\[\begin{split}g_D = \begin{cases} x^2 & 0\leq x\leq \frac{1}{2}\\ (x-1)^2 & \frac{1}{2}< x\leq 1 \end{cases} .\end{split}\]

The \(g_D\) is the exact solution of (178)-(180).

The jump \([\cdot]\) on the interface \(\Gamma\) is defined by

(182)\[\left[\frac{\partial u}{\partial \mathbf{n}}\right]=\nabla u_1\cdot\mathbf{n}_1+\nabla u_2\cdot\mathbf{n}_2,\label{var_ex-example}\]

where \(u_i\) is the solution in \(\Omega_i\) and the \(\mathbf{n}_i\) is the unit normal on \(\partial\Omega_i\cap\Gamma\).

As suggested in the original reference, this problem does not accept a strong (classical) solution but only a unique weak solution (\(g_D\)) which is shown in Fig. 102.


Please be advised that, in the original paper 1, the PDE is incorrect and (178)-(180) defines the corrected PDEs for the problem.

Since (180) suggests that the solution’s derivative is broken at interface (\(\Gamma\)) , you will have to do the variational form on \(\Omega_1\) and \(\Omega_2\) separately. Equations (183) and (184) show the continuous and discontinuous variational formulation for the problem above. For brevity, only the final variational forms are given here. For the detailed derivation of these formulations, see the Theory Appendix Derivation of Variational Form Example.

Variational form for Continuous type formulation :

(183)\[\int_{\Omega}(\nabla u\cdot\nabla v - fv) dx - \int_{\Gamma} g_Iv ds - \int_{\Gamma_D} \frac{\partial u}{\partial \mathbf{n}}v ds = 0\]

Variational form for Discontinuous type formulation :

(184)\[\sum_{i=1}^2(\nabla u_i\cdot v_i - fv_i) dx - \sum_{i=1}^2\int_{\Gamma_D}\frac{\partial u_i}{\partial \mathbf{n}} v_i ds-\int_{\Gamma}(g_I\langle v \rangle+\langle \nabla u \rangle[\![ v ]\!]) ds =0\]

The following subsections show how to implement these variational forms in the Modulus Sym.

This subsection shows how to implement the continuous type variational form (183) in Modulus Sym. The code for this example can be found in ./dg/

First, import all the packages needed:


import torch import modulus.sym from modulus.sym.hydra import instantiate_arch, ModulusConfig from modulus.sym.solver import Solver from modulus.sym.geometry import Bounds from modulus.sym.geometry.primitives_2d import Rectangle from modulus.sym.key import Key from modulus.sym.eq.pdes.diffusion import Diffusion from modulus.sym.utils.vpinn.test_functions import ( RBF_Function, Test_Function, Legendre_test, Trig_test, ) from modulus.sym.utils.vpinn.integral import tensor_int, Quad_Rect, Quad_Collection from modulus.sym.domain import Domain from modulus.sym.domain.constraint import ( PointwiseBoundaryConstraint, PointwiseInteriorConstraint, VariationalConstraint, ) from modulus.sym.dataset import DictVariationalDataset from modulus.sym.domain.validator import PointwiseValidator from modulus.sym.domain.inferencer import PointwiseInferencer from import ValidatorPlotter, InferencerPlotter from modulus.sym.loss import Loss from sympy import Symbol, Heaviside, Eq import numpy as np import quadpy

Creating the Geometry

Using the interface in the middle of the domain, you can define the geometry by left and right parts separately. This allows you to capture the interface information by sampling on the boundary that is common to the two halves.


# add constraints to solver x, y = Symbol("x"), Symbol("y") # make geometry rec_1 = Rectangle((0, 0), (0.5, 1)) rec_2 = Rectangle((0.5, 0), (1, 1)) rec = rec_1 + rec_2 # make training domain for traditional PINN eps = 0.02 rec_pinn = Rectangle((0 + eps, 0 + eps), (0.5 - eps, 1 - eps)) + Rectangle( (0.5 + eps, 0 + eps), (1 - eps, 1 - eps) )

In this example, you will use the variational form in conjunction with traditional PINNs. The PINNs’ loss is essentially a point-wise residual, and the loss function performs well for a smooth solution. Therefore, impose the traditional PINNs’ loss for areas away from boundaries and interfaces.

Defining the Boundary conditions and Equations to solve

With the geometry defined for the problem, you can define the constraints for the boundary conditions and PDEs.

The PDE will be taken care by variational form. However, there is no conflict to apply the classic form PDE constraints with the present of variational form. The rule of thumb is, with classic form PDE constraints, the neural network converges faster, but the computational graph is larger. The code segment below applies the classic form PDE constraint. This part is optional because of variational constraints.


# make domain domain = Domain() # PINN constraint # interior = PointwiseInteriorConstraint( # nodes=nodes, # geometry=rec_pinn, # outvar={"diffusion_u": 0}, # batch_size=4000, # bounds={x: (0 + eps, 1 - eps), y: (0 + eps, 1 - eps)}, # lambda_weighting={"diffusion_u": 1.}, # ) # domain.add_constraint(interior, "interior") # exterior boundary g = ((x - 1) ** 2 * Heaviside(x - 0.5)) + (x**2 * Heaviside(-x + 0.5)) boundary = PointwiseBoundaryConstraint( nodes=nodes, geometry=rec, outvar={"u": g}, batch_size=cfg.batch_size.boundary, lambda_weighting={"u": 10.0}, # weight edges to be zero criteria=~Eq(x, 0.5), ) domain.add_constraint(boundary, "boundary") batch_per_epoch = 100 variational_datasets = {} batch_sizes = {} # Middle line boundary invar = rec.sample_boundary( batch_per_epoch * cfg.batch_size.boundary, criteria=~Eq(x, 0.5) ) invar["area"] *= batch_per_epoch variational_datasets["boundary1"] = DictVariationalDataset( invar=invar, outvar_names=["u__x", "u__y"], ) batch_sizes["boundary1"] = cfg.batch_size.boundary # Middle line boundary invar = rec_1.sample_boundary( batch_per_epoch * cfg.batch_size.boundary, criteria=Eq(x, 0.5) ) invar["area"] *= batch_per_epoch variational_datasets["boundary2"] = DictVariationalDataset( invar=invar, outvar_names=["u__x"], ) batch_sizes["boundary2"] = cfg.batch_size.boundary # Interior points if paras = [ [ [[0, 0.5], [0, 1]], 20, True, lambda n: quadpy.c2.product(quadpy.c1.gauss_legendre(n)), ], [ [[0.5, 1], [0, 1]], 20, True, lambda n: quadpy.c2.product(quadpy.c1.gauss_legendre(n)), ], ] quad_rec = Quad_Collection(Quad_Rect, paras) invar = { "x": quad_rec.points_numpy[:, 0:1], "y": quad_rec.points_numpy[:, 1:2], "area": np.expand_dims(quad_rec.weights_numpy, -1), } variational_datasets["interior"] = DictVariationalDataset( invar=invar, outvar_names=["u__x", "u__y"], ) batch_sizes["interior"] = min( [quad_rec.points_numpy.shape[0], cfg.batch_size.interior] ) else: invar = rec.sample_interior( batch_per_epoch * cfg.batch_size.interior, bounds=Bounds({x: (0.0, 1.0), y: (0.0, 1.0)}), ) invar["area"] *= batch_per_epoch variational_datasets["interior"] = DictVariationalDataset( invar=invar, outvar_names=["u__x", "u__y"], ) batch_sizes["interior"] = cfg.batch_size.interior # Add points for RBF if == "rbf": invar = rec.sample_interior( batch_per_epoch * cfg.batch_size.rbf_functions, bounds=Bounds({x: (0.0, 1.0), y: (0.0, 1.0)}), ) invar["area"] *= batch_per_epoch variational_datasets["rbf"] = DictVariationalDataset( invar=invar, outvar_names=[], ) batch_sizes["rbf"] = cfg.batch_size.rbf_functions variational_constraint = VariationalConstraint( datasets=variational_datasets, batch_sizes=batch_sizes, nodes=nodes, num_workers=1, loss=DGLoss(, ) domain.add_constraint(variational_constraint, "variational")

For variational constraints, in the run function, first collect the data needed to formulate the variational form. For interior points, there are two options. The first option is quadrature rule. Modulus Sym has the functionality to create the quadrature rule on some basic geometries and meshes based on quadpy package. The quadrature rule has higher accuracy and efficiency, so use the quadrature rules when possible. The other option is using random points. You can use quasi-random points to increase the accuracy of the integral by setting quasirandom=True in sample_interior. For this examples, you can use cfg.quad in Hydra configure file to choose the option.

You can also use the radial basis test function. If so, use the additional data for the center of radial basis functions (RBFs).

Creating the Validator

Since the closed form solution is known, create a validator to compare the prediction and ground truth solution.


# add validation data delta_x = 0.01 delta_y = 0.01 x0 = np.arange(0, 1, delta_x) y0 = np.arange(0, 1, delta_y) x_grid, y_grid = np.meshgrid(x0, y0) x_grid = np.expand_dims(x_grid.flatten(), axis=-1) y_grid = np.expand_dims(y_grid.flatten(), axis=-1) u = np.where(x_grid <= 0.5, x_grid**2, (x_grid - 1) ** 2) invar_numpy = {"x": x_grid, "y": y_grid} outvar_numpy = {"u": u} openfoam_validator = PointwiseValidator( nodes=nodes, invar=invar_numpy, true_outvar=outvar_numpy, plotter=ValidatorPlotter(), ) domain.add_validator(openfoam_validator)

Creating the Inferencer

To generate the solution at the desired domain, add an inferencer.


# add inferencer data inferencer = PointwiseInferencer( nodes=nodes, invar=invar_numpy, output_names=["u"], batch_size=2048, plotter=InferencerPlotter(), ) domain.add_inferencer(inferencer)

Creating the Variational Loss

This subsection, shows how to form the variational loss. Use the data collected and registered to the VariationalConstraint to form this loss.

First, choose what test function to use.

In Modulus Sym, Legendre, 1st and 2nd kind of Chebyshev polynomials and trigonometric functions are already implemented as the test functions and can be selected directly. You can also define your own test functions by providing its name, domain, and SymPy expression in meta_test_function class. In the Test_Function, you will need to provide a dictionary of the name and order of the test functions (name_ord_dict in the parameter list), the upper and lower bound of your domain (box in the parameter list), and what kind of derivatives you will need (diff_list in the parameter list). For example, if \(v_{xxy}\) is needed, you might add [1,1,2] in the diff_list. There are shortcuts for diff_list. If you need all the components of gradient of test function, you might add 'grad' in diff_list, and if the Laplacian of the test function is needed, you might add 'Delta'. The box parameter if left unspecified, is set to the default values, i.e. for Legendre polynomials \([-1, 1]^n\), for trigonometric functions \([0, 1]^n\), etc.

The definition of test function will be put in initializer of the DGLoss class.


# custom variational loss class DGLoss(Loss): def __init__(self, test_function): super().__init__() # make test function self.test_function = test_function if test_function == "rbf": self.v = RBF_Function(dim=2, diff_list=["grad"]) self.eps = 10.0 elif test_function == "legendre": self.v = Test_Function( name_ord_dict={ Legendre_test: [k for k in range(10)], Trig_test: [k for k in range(5)], }, diff_list=["grad"], )

Then, it suffices to define the forward function of DGLoss. In forward, you need to form and return the variational loss. According to (183), the variational loss has been formed by the following code:


def forward( self, list_invar, list_outvar, step: int, ): # calculate test function if self.test_function == "rbf": v_outside = self.v.eval_test( "v", x=list_invar[0]["x"], y=list_invar[0]["y"], x_center=list_invar[3]["x"], y_center=list_invar[3]["y"], eps=self.eps, ) v_center = self.v.eval_test( "v", x=list_invar[1]["x"], y=list_invar[1]["y"], x_center=list_invar[3]["x"], y_center=list_invar[3]["y"], eps=self.eps, ) v_interior = self.v.eval_test( "v", x=list_invar[2]["x"], y=list_invar[2]["y"], x_center=list_invar[3]["x"], y_center=list_invar[3]["y"], eps=self.eps, ) vx_interior = self.v.eval_test( "vx", x=list_invar[2]["x"], y=list_invar[2]["y"], x_center=list_invar[3]["x"], y_center=list_invar[3]["y"], eps=self.eps, ) vy_interior = self.v.eval_test( "vy", x=list_invar[2]["x"], y=list_invar[2]["y"], x_center=list_invar[3]["x"], y_center=list_invar[3]["y"], eps=self.eps, ) elif self.test_function == "legendre": v_outside = self.v.eval_test( "v", x=list_invar[0]["x"], y=list_invar[0]["y"] ) v_center = self.v.eval_test("v", x=list_invar[1]["x"], y=list_invar[1]["y"]) v_interior = self.v.eval_test( "v", x=list_invar[2]["x"], y=list_invar[2]["y"] ) vx_interior = self.v.eval_test( "vx", x=list_invar[2]["x"], y=list_invar[2]["y"] ) vy_interior = self.v.eval_test( "vy", x=list_invar[2]["x"], y=list_invar[2]["y"] ) # calculate du/dn on surface dudn = ( list_invar[0]["normal_x"] * list_outvar[0]["u__x"] + list_invar[0]["normal_y"] * list_outvar[0]["u__y"] ) # form integrals of interior f = -2.0 uxvx = list_outvar[2]["u__x"] * vx_interior uyvy = list_outvar[2]["u__y"] * vy_interior fv = f * v_interior # calculate integrals int_outside = tensor_int(list_invar[0]["area"], v_outside, dudn) int_center = tensor_int(list_invar[1]["area"], 2.0 * v_center) int_interior = tensor_int(list_invar[2]["area"], uxvx + uyvy - fv) losses = { "variational_poisson": torch.abs(int_interior - int_center - int_outside) .pow(2) .sum() } return losses

list_invar includes all the inputs from the geometry while the list_outvar includes all requested outputs. The test function v can be evaluated by method v.eval_test. The parameters are: the name of function you want, and the coordinates to evaluate the functions.

Now, all the resulting variables of test function, like v_interior, are \(N\) by \(M\) tensors, where \(N\) is the number of points, and \(M\) is the number of the test functions.

To form the integration, you can use the tensor_int function in the Modulus Sym. This function has three parameters w, v, and u. The w is the quadrature weight for the integration. For uniform random points or quasi-random points, it is precisely the average area. The v is an \(N\) by \(M\) tensor, and u is a \(1\) by \(M\) tensor. If u is provided, this function will return a \(1\) by \(M\) tensor, and each entry is \(\int_\Omega u v_i dx\), for \(i=1,\cdots, M\). If u is not provided, it will return a \(1\) by \(M\) tensor, and each entry is \(\int_\Omega v_i dx\), for \(i=1,\cdots, M\).

Results and Post-processing

Solving the problem using different settings.

First, solve the problem by Legendre and trigonometric test function without quadrature rule. The results are shown in Fig. 103.


Fig. 103 Left: Modulus Sym. Center: Analytical. Right: Difference.

By using quadrature rule, the results are shown in Fig. 104.


Fig. 104 Left: Modulus Sym. Center: Analytical. Right: Difference.

By using quadrature rule and RBF test function, the results are shown in Fig. 105.


Fig. 105 Left: Modulus Sym. Center: Analytical. Right: Difference.

Weak formulation enables solution of PDEs with distributions, e.g., Dirac Delta function. The Dirac Delta function \(\delta(x)\) is defined as

(185)\[\int_{\mathbb{R}}f(x)\delta(x) dx = f(0),\]

for all continuous compactly supported functions \(f\).

This subsection solves the following problem:

(186)\[\begin{split}\begin{aligned} -\Delta u &= \delta \quad \mbox{ in } \Omega\\ u &= 0 \quad \text{ on } \partial\Omega\end{aligned}\end{split}\]

where \(\Omega=(-0.5,0.5)^2\) (Fig. 106). In physics, this means there is a point source in the middle of the domain with \(0\) Lebesgue measure in \(\mathbb{R}^2\). The corresponding weak formulation is

(187)\[\int_{\Omega}\nabla u\cdot \nabla v dx - \int_{\Gamma} \frac{\partial u}{\partial \mathbf{n}}v ds = v(0, 0)\]

The code of this example can be found in ./point_source/


Fig. 106 Domain for the point source problem.

Creating the Geometry

Use both the weak and differential form to solve (186) and (187). Since the solution has a sharp gradient around the origin, which causes issues for traditional PINNs, weight this area lower using the lambda weighting functions. The geometry can be defined by:


# make geometry rec = Rectangle((-0.5, -0.5), (0.5, 0.5))

Creating the Variational Loss and Solver

As shown in (187), the only difference to the previous examples is the right hand side term is the value of \(v\) instead of an integral. You only need to change the fv in the code. The whole code of the DGLoss is the following:


# make domain domain = Domain() Wall = PointwiseBoundaryConstraint( nodes=nodes, geometry=rec, outvar={"u": 0.0}, lambda_weighting={"u": 10.0}, batch_size=cfg.batch_size.boundary, fixed_dataset=False, batch_per_epoch=1, quasirandom=True, ) domain.add_constraint(Wall, name="OutsideWall") # PINN constraint interior = PointwiseInteriorConstraint( nodes=nodes, geometry=rec, outvar={"diffusion_u": 0.0}, batch_size=cfg.batch_size.interior, bounds=Bounds({x: (-0.5, 0.5), y: (-0.5, 0.5)}), lambda_weighting={"diffusion_u": (x**2 + y**2)}, fixed_dataset=False, batch_per_epoch=1, quasirandom=True, ) domain.add_constraint(interior, "interior") # Variational contraint variational = VariationalDomainConstraint( nodes=nodes, geometry=rec, outvar_names=["u__x", "u__y"], boundary_batch_size=cfg.batch_size.boundary, interior_batch_size=cfg.batch_size.interior, interior_bounds=Bounds({x: (-0.5, 0.5), y: (-0.5, 0.5)}), loss=DGLoss(), batch_per_epoch=1, quasirandom=True, ) domain.add_constraint(variational, "variational") # add inferencer data inferencer = PointwiseInferencer( nodes=nodes, invar=rec.sample_interior(10000), output_names=["u"], batch_size=2048, plotter=InferencerPlotter(), ) domain.add_inferencer(inferencer) # make solver slv = Solver(cfg, domain) # start solver slv.solve()

Results and Post-processing

The results for the problem are shown in Fig. 107.


Fig. 107 Modulus Sym prediction

Since the ground truth solution is unbounded at origin, it is not useful to compare it with the exact solution.


Zang, Y., Bao, G., Ye, X. and Zhou, H., 2020. Weak adversarial networks for high-dimensional partial differential equations. Journal of Computational Physics, 411, p.109409.

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