Dualspace (#70)

* Assembled 1-forms are now Cofunction in Firedrake * Expunge Vector from firedrake_adjoint
dolfin-adjoint · Sep 20, 2023 · 78b3bd6 · 78b3bd6
1 parent 8e524e4
commit 78b3bd6
Show file tree

Hide file tree

Showing 3 changed files with 10 additions and 6 deletions.
diff --git a/tests/firedrake_adjoint/test_assemble.py b/tests/firedrake_adjoint/test_assemble.py
@@ -55,7 +55,8 @@ def J(f):
         w1 = assemble(inner(f, v) * dx)
         w2 = assemble(inner(f**2, v) * dx)
         w3 = assemble(inner(f**3, v) * dx)
-        return assemble((w1 + w2 + w3)**2 * dx)
+        inner_dual = lambda x: assemble(action(x, x.riesz_representation()))
+        return sum(inner_dual(c) for c in (w1, w2, w3))
 
     _test_adjoint(J, f)
 
@@ -72,7 +73,8 @@ def test_assemble_1_forms_tlm():
     w1 = assemble(inner(f, v) * dx)
     w2 = assemble(inner(f**2, v) * dx)
     w3 = assemble(inner(f**3, v) * dx)
-    J = assemble((w1 + w2 + w3)**2 * dx)
+    inner_dual = lambda x: assemble(action(x, x.riesz_representation()))
+    J = sum(inner_dual(c) for c in (w1, w2, w3))
 
     Jhat = ReducedFunctional(J, Control(f))
     h = Function(V)
@@ -102,7 +104,7 @@ def _test_adjoint(J, f):
         Jm.block_variable.adj_value = 1.0
         tape.evaluate_adj()
 
-        dJdf = f.block_variable.adj_value
+        dJdf = f.block_variable.adj_value.vector()
 
         residual = abs(Jp - Jm - eps * dJdf.inner(h.vector()))
         residuals.append(residual)

diff --git a/tests/firedrake_adjoint/test_solving.py b/tests/firedrake_adjoint/test_solving.py
@@ -387,7 +387,7 @@ def _test_adjoint_constant(J, c):
         Jm.block_variable.adj_value = 1.0
         tape.evaluate_adj()
 
-        dJdc = c.block_variable.adj_value[0]
+        dJdc = c.block_variable.adj_value.vector()[0]
         print(dJdc)
 
         residual = abs(Jp - Jm - eps*dJdc)
@@ -419,7 +419,7 @@ def _test_adjoint(J, f):
         Jm.block_variable.adj_value = 1.0
         tape.evaluate_adj()
 
-        dJdf = f.block_variable.adj_value
+        dJdf = f.block_variable.adj_value.vector()
 
         residual = abs(Jp - Jm - eps*dJdf.inner(h.vector()))
         residuals.append(residual)

diff --git a/tests/firedrake_adjoint/test_tlm.py b/tests/firedrake_adjoint/test_tlm.py
@@ -61,7 +61,9 @@ def test_tlm_bc():
     J = assemble(c ** 2 * u * dx)
     Jhat = ReducedFunctional(J, Control(c))
 
-    c.block_variable.tlm_value = Constant(1)
+    # Need to specify the domain for the constant as `ufl.action`, which requires `ufl.Constant`
+    # to have a function space, will be applied on the tlm value.
+    c.block_variable.tlm_value = Constant(1, domain=mesh)
     tape.evaluate_tlm()
 
     assert (taylor_test(Jhat, Constant(c), Constant(1), dJdm=J.block_variable.tlm_value) > 1.9)