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66c9c8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | import warp as wp
from warp.fem.domain import GeometryDomain
from warp.fem.space import FunctionSpace, SpacePartition
from warp.fem.types import Sample, get_node_index_in_element
from warp.fem import utils, cache
from .field import SpaceField
class TrialField(SpaceField):
"""Field defined over a domain that can be used as a trial function"""
def __init__(
self,
space: FunctionSpace,
space_partition: SpacePartition,
domain: GeometryDomain,
):
if domain.dimension == space.dimension - 1:
space = space.trace()
if domain.dimension != space.dimension:
raise ValueError("Incompatible space and domain dimensions")
if not space.topology.is_derived_from(space_partition.space_topology):
raise ValueError("Incompatible space and space partition topologies")
super().__init__(space, space_partition)
self.domain = domain
self.EvalArg = self.space.SpaceArg
self.ElementEvalArg = self._make_element_eval_arg()
self.eval_degree = self._make_eval_degree()
self.eval_inner = self._make_eval_inner()
self.eval_grad_inner = self._make_eval_grad_inner()
self.eval_div_inner = self._make_eval_div_inner()
self.eval_outer = self._make_eval_outer()
self.eval_grad_outer = self._make_eval_grad_outer()
self.eval_div_outer = self._make_eval_div_outer()
self.at_node = self._make_at_node()
def partition_node_count(self) -> int:
"""Returns the number of nodes in the associated space topology partition"""
return self.space_partition.node_count()
@property
def name(self) -> str:
return self.space.name + "Trial"
def eval_arg_value(self, device) -> wp.codegen.StructInstance:
return self.space.space_arg_value(device)
def _make_element_eval_arg(self):
@cache.dynamic_struct(suffix=self.name)
class ElementEvalArg:
elt_arg: self.domain.ElementArg
eval_arg: self.EvalArg
return ElementEvalArg
def _make_eval_inner(self):
@cache.dynamic_func(suffix=self.name)
def eval_trial_inner(args: self.ElementEvalArg, s: Sample):
weight = self.space.element_inner_weight(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
return weight * self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof)
return eval_trial_inner
def _make_eval_grad_inner(self):
if not self.gradient_valid():
return None
@cache.dynamic_func(suffix=self.name)
def eval_nabla_trial_inner(args: self.ElementEvalArg, s: Sample):
nabla_weight = self.space.element_inner_weight_gradient(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
grad_transform = self.space.element_inner_reference_gradient_transform(args.elt_arg, s)
return utils.generalized_outer(
self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof),
utils.apply_right(nabla_weight, grad_transform),
)
return eval_nabla_trial_inner
def _make_eval_div_inner(self):
if not self.divergence_valid():
return None
@cache.dynamic_func(suffix=self.name)
def eval_div_trial_inner(args: self.ElementEvalArg, s: Sample):
nabla_weight = self.space.element_inner_weight_gradient(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
grad_transform = self.space.element_inner_reference_gradient_transform(args.elt_arg, s)
return utils.generalized_inner(
self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof),
utils.apply_right(nabla_weight, grad_transform),
)
return eval_div_trial_inner
def _make_eval_outer(self):
@cache.dynamic_func(suffix=self.name)
def eval_trial_outer(args: self.ElementEvalArg, s: Sample):
weight = self.space.element_outer_weight(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
return weight * self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof)
return eval_trial_outer
def _make_eval_grad_outer(self):
if not self.gradient_valid():
return None
@cache.dynamic_func(suffix=self.name)
def eval_nabla_trial_outer(args: self.ElementEvalArg, s: Sample):
nabla_weight = self.space.element_outer_weight_gradient(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
grad_transform = self.space.element_outer_reference_gradient_transform(args.elt_arg, s)
return utils.generalized_outer(
self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof),
utils.apply_right(nabla_weight, grad_transform),
)
return eval_nabla_trial_outer
def _make_eval_div_outer(self):
if not self.divergence_valid():
return None
@cache.dynamic_func(suffix=self.name)
def eval_div_trial_outer(args: self.ElementEvalArg, s: Sample):
nabla_weight = self.space.element_outer_weight_gradient(
args.elt_arg,
args.eval_arg,
s.element_index,
s.element_coords,
get_node_index_in_element(s.trial_dof),
)
grad_transform = self.space.element_outer_reference_gradient_transform(args.elt_arg, s)
return utils.generalized_inner(
self.space.unit_dof_value(args.elt_arg, args.eval_arg, s.trial_dof),
utils.apply_right(nabla_weight, grad_transform),
)
return eval_div_trial_outer
def _make_at_node(self):
@cache.dynamic_func(suffix=self.name)
def at_node(args: self.ElementEvalArg, s: Sample):
node_coords = self.space.node_coords_in_element(
args.elt_arg, args.eval_arg, s.element_index, get_node_index_in_element(s.trial_dof)
)
return Sample(s.element_index, node_coords, s.qp_index, s.qp_weight, s.test_dof, s.trial_dof)
return at_node
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