tutorial11 - Equation Class

Author: AleDinve

docs/source/_rst/_tutorial.rst

@@ -21,7 +21,8 @@ Physics Informed Neural Networks
    Two dimensional Poisson problem using Extra Features Learning<tutorials/tutorial2/tutorial.rst>
    Two dimensional Wave problem with hard constraint<tutorials/tutorial3/tutorial.rst>
    Resolution of a 2D Poisson inverse problem<tutorials/tutorial7/tutorial.rst>
-   Periodic Boundary Conditions for Helmotz Equation<tutorials/tutorial9/tutorial.rst>
+   Periodic Boundary Conditions for Helmholtz Equation<tutorials/tutorial9/tutorial.rst>
+   Building custom equations with PINA Equation class<tutorials/tutorial11/tutorial.rst>
 
 Neural Operator Learning
 ------------------------

docs/source/_rst/tutorials/tutorial11/tutorial.rst

@@ -0,0 +1,162 @@
+Tutorial: The ``Equation`` Class
+================================
+
+In this tutorial, we will show how to use the ``Equation`` Class in
+PINA. Specifically, we will see how use the Class and its inherited
+classes to enforce residuals minimization in PINNs.
+
+Example: The Burgers 1D equation
+================================
+
+We will start implementing the viscous Burgers 1D problem Class,
+described as follows:
+
+.. math::
+
+
+   \begin{equation}
+   \begin{cases}
+   \frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} &= \nu \frac{\partial^2 u}{ \partial x^2}, \quad x\in(0,1), \quad t>0\\
+   u(x,0) &= -\sin (\pi x)\\
+   u(x,t) &= 0 \quad x = \pm 1\\
+   \end{cases}
+   \end{equation}
+
+where we set $ :raw-latex:`\nu `= :raw-latex:`\frac{0.01}{\pi}`$.
+
+In the class that models this problem we will see in action the
+``Equation`` class and one of its inherited classes, the ``FixedValue``
+class.
+
+.. code:: ipython3
+
+    #useful imports
+    from pina.problem import SpatialProblem, TimeDependentProblem
+    from pina.equation import Equation, FixedValue, FixedGradient, FixedFlux
+    from pina.geometry import CartesianDomain
+    import torch
+    from pina.operators import grad, laplacian
+    from pina import Condition
+    
+
+
+.. code:: ipython3
+
+    class Burgers1D(TimeDependentProblem, SpatialProblem):
+    
+        # define the burger equation
+        def burger_equation(input_, output_):
+            du = grad(output_, input_)
+            ddu = grad(du, input_, components=['dudx'])
+            return (
+                du.extract(['dudt']) +
+                output_.extract(['u'])*du.extract(['dudx']) -
+                (0.01/torch.pi)*ddu.extract(['ddudxdx'])
+            )
+    
+        # define initial condition
+        def initial_condition(input_, output_):
+            u_expected = -torch.sin(torch.pi*input_.extract(['x']))
+            return output_.extract(['u']) - u_expected
+    
+        # assign output/ spatial and temporal variables
+        output_variables = ['u']
+        spatial_domain = CartesianDomain({'x': [-1, 1]})
+        temporal_domain = CartesianDomain({'t': [0, 1]})
+    
+        # problem condition statement
+        conditions = {
+            'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)),
+            'gamma2': Condition(location=CartesianDomain({'x':  1, 't': [0, 1]}), equation=FixedValue(0.)),
+            't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)),
+            'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Equation(burger_equation)),
+        }
+
+The ``Equation`` class takes as input a function (in this case it
+happens twice, with ``initial_condition`` and ``burger_equation``) which
+computes a residual of an equation, such as a PDE. In a problem class
+such as the one above, the ``Equation`` class with such a given input is
+passed as a parameter in the specified ``Condition``.
+
+The ``FixedValue`` class takes as input a value of same dimensions of
+the output functions; this class can be used to enforced a fixed value
+for a specific condition, e.g. Dirichlet boundary conditions, as it
+happens for instance in our example.
+
+Once the equations are set as above in the problem conditions, the PINN
+solver will aim to minimize the residuals described in each equation in
+the training phase.
+
+Available classes of equations include also: - ``FixedGradient`` and
+``FixedFlux``: they work analogously to ``FixedValue`` class, where we
+can require a constant value to be enforced, respectively, on the
+gradient of the solution or the divergence of the solution; -
+``Laplace``: it can be used to enforce the laplacian of the solution to
+be zero; - ``SystemEquation``: we can enforce multiple conditions on the
+same subdomain through this class, passing a list of residual equations
+defined in the problem.
+
+Defining a new Equation class
+=============================
+
+``Equation`` classes can be also inherited to define a new class. As
+example, we can see how to rewrite the above problem introducing a new
+class ``Burgers1D``; during the class call, we can pass the viscosity
+parameter :math:`\nu`:
+
+.. code:: ipython3
+
+    class Burgers1DEquation(Equation):
+        
+        def __init__(self, nu = 0.):
+            """
+            Burgers1D class. This class can be
+            used to enforce the solution u to solve the viscous Burgers 1D Equation.
+            
+            :param torch.float32 nu: the viscosity coefficient. Default value is set to 0.
+            """
+            self.nu = nu 
+        
+            def equation(input_, output_):
+                    return grad(output_, input_, d='x') +\
+                           output_*grad(output_, input_, d='t') -\
+                           self.nu*laplacian(output_, input_, d='x')
+    
+                
+            super().__init__(equation)
+
+Now we can just pass the above class as input for the last condition,
+setting :math:`\nu= \frac{0.01}{\pi}`:
+
+.. code:: ipython3
+
+    class Burgers1D(TimeDependentProblem, SpatialProblem):
+    
+        # define initial condition
+        def initial_condition(input_, output_):
+            u_expected = -torch.sin(torch.pi*input_.extract(['x']))
+            return output_.extract(['u']) - u_expected
+    
+        # assign output/ spatial and temporal variables
+        output_variables = ['u']
+        spatial_domain = CartesianDomain({'x': [-1, 1]})
+        temporal_domain = CartesianDomain({'t': [0, 1]})
+    
+        # problem condition statement
+        conditions = {
+            'gamma1': Condition(location=CartesianDomain({'x': -1, 't': [0, 1]}), equation=FixedValue(0.)),
+            'gamma2': Condition(location=CartesianDomain({'x':  1, 't': [0, 1]}), equation=FixedValue(0.)),
+            't0': Condition(location=CartesianDomain({'x': [-1, 1], 't': 0}), equation=Equation(initial_condition)),
+            'D': Condition(location=CartesianDomain({'x': [-1, 1], 't': [0, 1]}), equation=Burgers1DEquation(0.01/torch.pi)),
+        }
+
+What’s next?
+============
+
+Congratulations on completing the ``Equation`` class tutorial of
+**PINA**! As we have seen, you can build new classes that inherits
+``Equation`` to store more complex equations, as the Burgers 1D
+equation, only requiring to pass the characteristic coefficients of the
+problem. From now on, you can: - define additional complex equation
+classes (e.g. ``SchrodingerEquation``, ``NavierStokeEquation``..) -
+define more ``FixedOperator`` (e.g. ``FixedCurl``)