Update AIMS schedule (#4216)

* Add tape.svg image
* Add links to Devtio
* Remove dead links

Author: JDBetteridge

demos/eigenvalues_QG_basinmodes/qgbasinmodes.py.rst

@@ -138,7 +138,7 @@ We define the Test Function :math:`\phi` and the Trial Function
 To build the weak formulation of our equation we need to build two PETSc
 matrices in the form of a generalized eigenvalue problem,
 :math:`A\psi = \lambda M\psi`. This eigenproblem takes `restrict=True` to help
-users to avoid convergence failures by removing eigenvalues on the 
+users to avoid convergence failures by removing eigenvalues on the
 boundary, while preserving the original function space for the eigenmodes. ::
 
   eigenproblem = LinearEigenproblem(

docs/source/adjoint.rst

@@ -81,7 +81,7 @@ Consider now the function signatures of the symbols in :eq:`eq:djdm`. Here we ar
 only concerned with the arguments, as these determine the sizes of the
 resulting assembled tensors:
 
-.. math:: 
+.. math::
     :label:
 
     \frac{\mathrm{d}\hat{J}}{\mathrm{d}m}: M\rightarrow \mathbb{R}
@@ -102,16 +102,16 @@ Instead, we define:
 
 .. math::
     :label: eq:adjoint
-    
+
     \lambda^*(\in V\rightarrow\mathbb{R}) = -\frac{\partial J}{\partial u}\frac{\partial f}{\partial u}^{-1}.
 
 We actually solve the adjoint to this equation. That is find `\lambda \in V`
-such that: 
+such that:
 
 .. math::
     :label:
 
-    \frac{\partial f}{\partial u}^{*}(u, m; \lambda, v) = 
+    \frac{\partial f}{\partial u}^{*}(u, m; \lambda, v) =
     -\frac{\partial J}{\partial u}(u, m; v) \qquad \forall v \in V.
 
 Note that these terms include `u`, so it is first necessary to solve
@@ -144,8 +144,8 @@ How Firedrake and Pyadjoint automate derivative calculation
 -----------------------------------------------------------
 
 Firedrake automates the process in the preceding section using the methodology
-first published in :cite:`Farrell2013` using the implementation in 
-`Pyadjoint <https://pyadjoint.org>`__ :cite:`Mitusch2019`. 
+first published in :cite:`Farrell2013` using the implementation in
+`Pyadjoint <https://pyadjoint.org>`__ :cite:`Mitusch2019`.
 
 The essence of this process is:
 
@@ -273,7 +273,7 @@ The sequence of recorded operations is stored on an object called the tape. The
 currently active tape can be accessed by calling
 :func:`~pyadjoint.get_working_tape`. The user usually has limited direct
 interaction with the tape, but there is some useful information which can be
-extracted. 
+extracted.
 
 Visualising the tape
 ~~~~~~~~~~~~~~~~~~~~
@@ -289,11 +289,11 @@ are to be found on `the pygraphviz website
 
 .. _fig-tape:
 
-.. figure:: images/tape.pdf
+.. figure:: images/tape.svg
 
     A visualisation of the Burgers equation example above shortened to a single
     timestep. Operations (blocks) recorded on the tape are shown as grey
-    rectangles, while taped variables are shown as ovals. 
+    rectangles, while taped variables are shown as ovals.
 
 The numbered blocks in the tape visualisation are as follows:
 
@@ -423,4 +423,4 @@ themselves with this since annotated operations will return overloaded types.
     :filter: False
 
     Farrell2013
-    Mitusch2019
+    Mitusch2019

docs/source/aims_25.rst

@@ -22,7 +22,7 @@ reading is available on the links above.
 
 .. list-table::
 
-  * - 
+  * -
     - **Tuesday**
     - **Wednesday**
     - **Thursday**
@@ -32,7 +32,7 @@ reading is available on the links above.
     - `Function spaces and inner products <https://wp.doc.ic.ac.uk/spo/wp-content/uploads/sites/31/2013/11/notes.pdf>`__
     - `The Ciarlet finite element <https://finite-element.github.io/L2_fespaces.html>`__
     - `Nonlinear problems <https://finite-element.github.io/8_nonlinear_problems.html>`__
-    - Devito: inverse tomography
+    - Devito: `sympy <https://colab.research.google.com/github/devitocodes/devito/blob/JDBetteridge%2Faims_25/examples/userapi/00_sympy.ipynb>`__, `Devito <https://colab.research.google.com/github/devitocodes/devito/blob/JDBetteridge%2Faims_25/examples/userapi/01_dsl.ipynb>`__, `Acoustic Wave equation <https://colab.research.google.com/github/devitocodes/devito/blob/JDBetteridge%2Faims_25/examples/seismic/tutorials/01_modelling.ipynb>`__
     - Software for adjoints: differentiable programming
   * - **Late AM**
     - `Our first finite element problem <https://finite-element.github.io/L1_introduction.html>`__
@@ -44,13 +44,13 @@ reading is available on the links above.
     - Firedrake practical 1: `Helmholtz Equation <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/01-spd-helmholtz.ipynb>`__
     - `Systems with more than one variable <https://finite-element.github.io/9_mixed_problems.html>`__
     - An introduction to solvers and preconditioners
-    - Devito: inverse tomography
+    - Devito: `Full Waveform Inversion <https://colab.research.google.com/github/devitocodes/devito/blob/JDBetteridge%2Faims_25/examples/seismic/tutorials/03_fwi.ipynb>`__
     - `Deep learning introduction <https://github.com/NBoulle/physics-driven-ml/raw/main/tutorials/intro_deep_learning.pdf>`__, `practical <https://colab.research.google.com/github/NBoulle/physics-driven-ml/blob/main/tutorials/Tutorial_1_ex.ipynb>`__
   * - **Late PM**
     - Firedrake practical 2: `Dirichlet boundary conditions <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/02-poisson.ipynb>`__
     - Firedrake practical 4: `mixed Poisson <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/05-mixed-poisson.ipynb>`__
     - Firedrake practical 6: `solvers <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/08-composable-solvers.ipynb>`__
-    - Adjoint practical 1: `Burgers again <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/13_adjoint_calculations.ipynb#scrollTo=e7b5808f-f0ae-4e40-bcca-eff67f251fc9>`__
+    - Adjoint practical 1: `Burgers again <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/aims/13_adjoint_calculations.ipynb>`__
     - `Scientific machine learning <https://github.com/NBoulle/physics-driven-ml/raw/main/tutorials/sciml_intro.pdf>`__, `practical <https://colab.research.google.com/github/NBoulle/physics-driven-ml/blob/main/tutorials/Tutorial_2_ex.ipynb>`__