Merge branch 'main' into hypervisco

optimism/EquationSolver.py

@@ -564,7 +564,7 @@ def newton_solve(objective_func, solution, maxSteps=1):
         with Timer(name="Step"):
             with Timer(name="Fill Grad"):
                 gradients = grad_func(solution)
-                print('res = ', np.linalg.norm(gradients))
+                print('step: ', step, 'res = ', np.linalg.norm(gradients))
 
             with Timer(name="Fill Hess"):
                 hess = hess_func(solution)

optimism/test/test_PatchTestPou.py

@@ -0,0 +1,246 @@
+import jax
+import jax.numpy as np
+import numpy as onp
+
+from optimism import FunctionSpace
+from optimism.material import LinearElastic as MatModel
+#from optimism.material import Neohookean as MatModel
+from optimism import Mesh
+from optimism import Mechanics
+from optimism.EquationSolver import newton_solve
+from optimism import QuadratureRule
+from optimism.test import MeshFixture
+from optimism.TensorMath import tensor_2D_to_3D
+import metis
+
+E = 1.0
+nu = 0.3
+props = {'elastic modulus': E,
+         'poisson ratio': nu,
+         'strain measure': 'linear'}
+
+
+def insort(a, b, kind='mergesort'):
+    c = onp.concatenate((a, b))
+    c.sort(kind=kind)
+    flag = onp.ones(len(c), dtype=bool)
+    onp.not_equal(c[1:], c[:-1], out=flag[1:])
+    return c[flag]
+
+
+def create_graph(conns):
+
+    elemToElem = [ [] for _ in range(len(conns)) ]
+    _, edges = Mesh.create_edges(conns)
+    for edge in edges:
+        t0 = edge[0]
+        t1 = edge[2]
+        if t1 > -1:
+            elemToElem[t0].append(t1)
+            elemToElem[t1].append(t0)
+
+    #return elemToElem
+
+    nodeToElem = {}
+    for e, elem in enumerate(conns):
+        for n in elem:
+            n = int(n)
+            if n in nodeToElem:
+                nodeToElem[n].append(e)
+            else:
+                nodeToElem[n] = [e]
+
+    elemToElem = [None] * len(conns)
+    for e, elem in enumerate(conns):
+        elemToElem[e] = nodeToElem[int(elem[0])]
+        for n in elem[1:]:
+            n = int(n)
+            elemToElem[e] = insort(elemToElem[e], nodeToElem[n])
+        elemToElem[e] = list(elemToElem[e])
+
+    return elemToElem
+
+
+def create_partitions(conns):
+    graph = create_graph(conns)
+    (edgecuts, parts) = metis.part_graph(graph, 4)
+    return np.array(parts, dtype=int)
+
+
+def construct_basis_on_poly(elems, conns, fs : FunctionSpace.FunctionSpace):
+    uniqueNodes = conns[elems[0]]
+    for e in elems[1:]:
+        uniqueNodes = insort(uniqueNodes, conns[e])
+
+    # these are not set to be general yet, assuming the pou integration is over all nodes, all nodes are active
+    Q = len(uniqueNodes) # number of quadrature bases
+    Nnode = len(uniqueNodes) # number of active nodes on poly
+
+    globalNodeToLocalNode = {nodeN : n for n, nodeN in enumerate(uniqueNodes)}
+
+    G = onp.zeros((Q,Q))
+    for e in elems:
+        elemQuadratureShapes = fs.shapes[e]
+        vols = fs.vols[e]
+        for n, node in enumerate(conns[e]):
+            node = int(node)
+            for m, mode in enumerate(conns[e]):
+                mode = int(mode)
+                N = globalNodeToLocalNode[node]
+                M = globalNodeToLocalNode[mode]
+                G[N,M] += vols @ (elemQuadratureShapes[:,n] * elemQuadratureShapes[:,m])
+
+    S,U = onp.linalg.eigh(G)
+    nonzeroS = abs(S) > 1e-14
+    Sinv = 1.0/S[nonzeroS]
+    Uu = U[:, nonzeroS]
+
+    Ginv = Uu@onp.diag(Sinv)@Uu.T
+
+    dim = fs.shapeGrads.shape[3]
+    B = onp.zeros((Q, Nnode, fs.shapeGrads.shape[3]))
+    for e in elems:
+        elemQuadratureShapes = fs.shapes[e]
+        elemShapeGrads = fs.shapeGrads[e]
+
+        vols = fs.vols[e]
+        for n, node in enumerate(conns[e]):
+            node = int(node)
+            for m, mode in enumerate(conns[e]):
+                mode = int(mode)
+                N = globalNodeToLocalNode[node]
+                M = globalNodeToLocalNode[mode]
+                for d in range(dim):
+                    B[N,M,d] += vols @ (elemQuadratureShapes[:,n] * elemShapeGrads[:,m,d])
+
+    for n in range(Nnode):
+        for d in range(dim):
+            B[:,n,d] = Ginv@B[:,n,d]
+
+    # attemps to diagonalize the Graham matrix
+    #G1 = onp.average(G, axis=1)
+    #GinvG1 = Ginv @ G1
+
+    #V = onp.outer( onp.ones(Nnode), GinvG1 )
+    #Gtilde = V @ G @ V.T
+
+    #print('Gtilesum = ', onp.sum(onp.sum(Gtilde,axis=0),axis=0))
+    #print('Gsum = ', onp.sum(onp.sum(G,axis=0),axis=0))
+    #print('Gtilde = ', Gtilde)
+
+    return B, np.sum(G, axis=1), globalNodeToLocalNode
+
+
+class PatchTestQuadraticElements(MeshFixture.MeshFixture):
+
+    def setUp(self):
+        self.Nx = 5
+        self.Ny = 5
+
+        xRange = [0.,1.]
+        yRange = [0.,1.]
+        self.mesh, _ = self.create_mesh_and_disp(self.Nx, self.Ny, xRange, yRange, lambda x : x)
+        self.partition = create_partitions(self.mesh.conns) # partitioning seems to only work on lower order mesh at the moment
+        #self.mesh = Mesh.create_higher_order_mesh_from_simplex_mesh(self.mesh, order=2, createNodeSetsFromSideSets=True)
+
+        self.quadRule = QuadratureRule.create_quadrature_rule_on_triangle(degree=2)
+        self.fs = FunctionSpace.construct_function_space(self.mesh, self.quadRule)
+        self.materialModel = MatModel.create_material_model_functions(props)
+
+
+    def create_polys(self):
+        polys = {}
+        for e,p in enumerate(self.partition):
+            p = int(p)
+            e = int(e)
+            if p in polys:
+                polys[p].append(e)
+            else:
+                polys[p] = [e]
+        return polys
+
+
+    def write_output(self, Uu, Ubc, step):
+        from optimism import VTKWriter
+        U = self.dofManager.create_field(Uu, Ubc)
+        plotName = 'patch-'+str(step).zfill(3)
+        writer = VTKWriter.VTKWriter(self.mesh, baseFileName=plotName)
+
+        writer.add_nodal_field(name='displacement', nodalData=U, fieldType=VTKWriter.VTKFieldType.VECTORS)
+
+        bcs = np.array(self.dofManager.isBc, dtype=int)
+        writer.add_nodal_field(name='bcs', nodalData=bcs, fieldType=VTKWriter.VTKFieldType.VECTORS, dataType=VTKWriter.VTKDataType.INT)
+
+        writer.add_cell_field(name='partition',
+                              cellData=self.partition,
+                              fieldType=VTKWriter.VTKFieldType.SCALARS)
+        writer.write()
+
+
+    def test_dirichlet_patch_test_with_quadratic_elements(self):
+        ebcs = [FunctionSpace.EssentialBC(nodeSet='all_boundary', component=0),
+                FunctionSpace.EssentialBC(nodeSet='all_boundary', component=1)]
+        self.dofManager = FunctionSpace.DofManager(self.fs, self.mesh.coords.shape[1], ebcs)
+
+        self.targetDispGrad = np.array([[0.1, -0.2],[0.4, -0.1]]) 
+        self.UTarget = self.mesh.coords@self.targetDispGrad.T
+        Ubc = self.dofManager.get_bc_values(self.UTarget)
+
+        polys = self.create_polys()
+        polyFs = []
+
+        totalVol = 0.0
+        for p in polys:
+            pElems = polys[p]
+            B,W,globalToLocalNode = construct_basis_on_poly(pElems, self.mesh.conns, self.fs)
+            polyFs.append((B,W,globalToLocalNode))
+            pvol = np.sum(W)
+            totalVol += pvol
+            gradUs = np.zeros((B.shape[0],B.shape[2],B.shape[2]))
+            for node in globalToLocalNode:
+                n = globalToLocalNode[node]
+                b = B[:,n,:]
+                UatN = self.UTarget[node]
+                gradUs += jax.vmap(lambda bb : np.outer(UatN,bb))(b)
+
+            for gradU in gradUs:
+                self.assertArrayNear(gradU, self.targetDispGrad, 8)   
+
+        def objective_poly(Uu):
+
+            def energy_density(gradu):
+                g = tensor_2D_to_3D(gradu)
+                return self.materialModel.compute_energy_density(g, None, 0.0)
+
+            U = self.dofManager.create_field(Uu, Ubc)
+            totalEnergy = 0.0
+            for B,W,gToLMap in polyFs:
+                gradU = np.zeros((B.shape[0],B.shape[2],B.shape[2]))
+                for node in gToLMap:
+                    n = gToLMap[node]
+                    b = B[:,n,:]
+                    UatN = U[node]
+                    gradU += jax.vmap(lambda bb : np.outer(UatN,bb))(b)
+
+                energies = jax.vmap(energy_density)(gradU)
+                totalEnergy += W @ energies
+
+            return totalEnergy
+
+        Uu = newton_solve(objective_poly, self.dofManager.get_unknown_values(0.0*self.UTarget))
+
+        self.write_output(Uu, Ubc, 0)
+
+        U = self.dofManager.create_field(Uu, Ubc)
+        dispGrads = FunctionSpace.compute_field_gradient(self.fs, U)
+        ne, nqpe = self.fs.vols.shape
+        for dg in dispGrads.reshape(ne*nqpe,2,2):
+            self.assertArrayNear(dg, self.targetDispGrad, 14)
+
+        grad_func = jax.grad(objective_poly)
+        self.assertNear(np.linalg.norm(grad_func(Uu)), 0.0, 14)
+
+
+if __name__ == '__main__':
+    import unittest
+    unittest.main()

setup.py

@@ -8,7 +8,8 @@
     install_requires=['jax[cpu]',
                       'scipy',
                       'matplotlib', # this is not strictly necessary
-                      'netcdf4'],
+                      'netcdf4',
+                      'metis'],
     #tests_require=[], # could put chex and pytest here
     extras_require={'sparse': ['scikit-sparse'],
                     'test': ['pytest', 'pytest-cov', 'pytest-xdist']},