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Merge pull request #295 from HydrogenSulfate/add_darcy2d
Add darcy2d
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‎docs/images/darcy2d/darcy2d_v2.png

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‎docs/zh/api/equation.md

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- Laplace
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- NavierStokes
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- NormalDotVec
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- Poisson
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show_root_heading: false
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heading_level: 3

‎examples/darcy/darcy2d_v2.py

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import ppsci
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from ppsci.utils import config
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from ppsci.utils import logger
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if __name__ == "__main__":
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args = config.parse_args()
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# set random seed for reproducibility
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ppsci.utils.misc.set_random_seed(42)
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# set output directory
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output_dir = "./output_darcy2d" if not args.output_dir else args.output_dir
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# initialize logger
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logger.init_logger("ppsci", f"{output_dir}/train.log", "info")
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# set model
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model = ppsci.arch.MLP(("x", "y"), ("p",), 5, 20, "tanh", False, False)
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# set equation
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equation = {"Poisson": ppsci.equation.Poisson(2)}
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# set geometry
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geom = {"rect": ppsci.geometry.Rectangle((0.0, 0.0), (1.0, 1.0))}
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# set dataloader config
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ITERS_PER_EPOCH = 1
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train_dataloader_cfg = {
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"dataset": "IterableNamedArrayDataset",
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"iters_per_epoch": ITERS_PER_EPOCH,
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}
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NPOINT_PDE = 99**2
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NPOINT_TOP = 101
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NPOINT_BOTTOM = 101
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NPOINT_LEFT = 99
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NPOINT_RIGHT = 99
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# set constraint
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def poisson_ref_compute_func(_in):
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return (
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-8.0
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* (np.pi**2)
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* np.sin(2.0 * np.pi * _in["x"])
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* np.cos(2.0 * np.pi * _in["y"])
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)
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pde_constraint = ppsci.constraint.InteriorConstraint(
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equation["Poisson"].equations,
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{"poisson": poisson_ref_compute_func},
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geom["rect"],
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{**train_dataloader_cfg, "batch_size": NPOINT_PDE},
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ppsci.loss.MSELoss("sum"),
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evenly=True,
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name="EQ",
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)
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bc_top = ppsci.constraint.BoundaryConstraint(
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{"p": lambda out: out["p"]},
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{
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"p": lambda _in: np.sin(2.0 * np.pi * _in["x"])
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* np.cos(2.0 * np.pi * _in["y"])
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},
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geom["rect"],
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{**train_dataloader_cfg, "batch_size": NPOINT_TOP},
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ppsci.loss.MSELoss("sum"),
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criteria=lambda x, y: np.isclose(y, 1.0),
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name="BC_top",
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)
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bc_bottom = ppsci.constraint.BoundaryConstraint(
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{"p": lambda out: out["p"]},
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{
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"p": lambda _in: np.sin(2.0 * np.pi * _in["x"])
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* np.cos(2.0 * np.pi * _in["y"])
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},
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geom["rect"],
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{**train_dataloader_cfg, "batch_size": NPOINT_BOTTOM},
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ppsci.loss.MSELoss("sum"),
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criteria=lambda x, y: np.isclose(y, 0.0),
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name="BC_bottom",
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)
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bc_left = ppsci.constraint.BoundaryConstraint(
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{"p": lambda out: out["p"]},
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{
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"p": lambda _in: np.sin(2.0 * np.pi * _in["x"])
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* np.cos(2.0 * np.pi * _in["y"])
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},
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geom["rect"],
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{**train_dataloader_cfg, "batch_size": NPOINT_LEFT},
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ppsci.loss.MSELoss("sum"),
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criteria=lambda x, y: np.isclose(x, 0.0),
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name="BC_left",
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)
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bc_right = ppsci.constraint.BoundaryConstraint(
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{"p": lambda out: out["p"]},
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{
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"p": lambda _in: np.sin(2.0 * np.pi * _in["x"])
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* np.cos(2.0 * np.pi * _in["y"])
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},
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geom["rect"],
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{**train_dataloader_cfg, "batch_size": NPOINT_RIGHT},
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ppsci.loss.MSELoss("sum"),
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criteria=lambda x, y: np.isclose(x, 1.0),
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name="BC_right",
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)
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# wrap constraints together
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constraint = {
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pde_constraint.name: pde_constraint,
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bc_top.name: bc_top,
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bc_bottom.name: bc_bottom,
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bc_left.name: bc_left,
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bc_right.name: bc_right,
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}
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# set training hyper-parameters
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epochs = 10000 if not args.epochs else args.epochs
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# set optimizer
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optimizer = ppsci.optimizer.Adam(1e-3)((model,))
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# set validator
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NPOINTS_EVAL = NPOINT_PDE
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residual_validator = ppsci.validate.GeometryValidator(
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equation["Poisson"].equations,
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{"poisson": poisson_ref_compute_func},
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geom["rect"],
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{
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"dataset": "NamedArrayDataset",
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"total_size": NPOINTS_EVAL,
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"batch_size": 8192,
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"sampler": {"name": "BatchSampler"},
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},
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ppsci.loss.MSELoss("sum"),
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evenly=True,
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metric={"MSE": ppsci.metric.MSE()},
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name="Residual",
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)
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validator = {residual_validator.name: residual_validator}
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# set visualizer(optional)
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# manually collate input data for visualization,
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NPOINT_BC = NPOINT_TOP + NPOINT_BOTTOM + NPOINT_LEFT + NPOINT_RIGHT
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vis_points = geom["rect"].sample_interior(NPOINT_PDE + NPOINT_BC, evenly=True)
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visualizer = {
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"visulzie_u_v": ppsci.visualize.VisualizerVtu(
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vis_points,
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{"p": lambda d: d["p"]},
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prefix="result_u_v",
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)
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}
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# initialize solver
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solver = ppsci.solver.Solver(
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model,
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constraint,
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output_dir,
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optimizer,
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None,
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epochs,
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ITERS_PER_EPOCH,
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eval_during_train=True,
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eval_freq=200,
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equation=equation,
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geom=geom,
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validator=validator,
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visualizer=visualizer,
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)
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# train model
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solver.train()
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# evaluate after finished training
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solver.eval()
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# visualize prediction after finished training
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solver.visualize()
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# directly evaluate pretrained model(optional)
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logger.init_logger("ppsci", f"{output_dir}/eval.log", "info")
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solver = ppsci.solver.Solver(
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model,
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constraint,
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output_dir,
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equation=equation,
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geom=geom,
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validator=validator,
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visualizer=visualizer,
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pretrained_model_path=f"{output_dir}/checkpoints/latest",
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)
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solver.eval()
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# visualize prediction for pretrained model(optional)
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solver.visualize()

‎ppsci/equation/__init__.py

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from ppsci.equation.pde import Laplace
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from ppsci.equation.pde import NavierStokes
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from ppsci.equation.pde import NormalDotVec
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from ppsci.equation.pde import Poisson
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from ppsci.equation.pde import Vibration
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from ppsci.utils import logger
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from ppsci.utils import misc
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"Laplace",
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"NavierStokes",
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"NormalDotVec",
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"Poisson",
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"Vibration",
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"build_equation",
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]

‎ppsci/equation/pde/__init__.py

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from ppsci.equation.pde.laplace import Laplace
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from ppsci.equation.pde.navier_stokes import NavierStokes
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from ppsci.equation.pde.normal_dot_vec import NormalDotVec
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from ppsci.equation.pde.poisson import Poisson
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from ppsci.equation.pde.viv import Vibration
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__all__ = [
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"Laplace",
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"NavierStokes",
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"NormalDotVec",
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"Poisson",
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"Vibration",
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]

‎ppsci/equation/pde/poisson.py

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ppsci.autodiff import hessian
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from ppsci.equation.pde import base
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class Poisson(base.PDE):
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"""Poisson
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Args:
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dim (int): Dimension of equation.
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Examples:
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>>> import ppsci
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>>> pde = ppsci.equation.Poisson(2)
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"""
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def __init__(self, dim: int):
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super().__init__()
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self.dim = dim
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def poisson_compute_func(out):
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invars = ("x", "y", "z")[: self.dim]
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poisson = 0
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for invar in invars:
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poisson += hessian(out["p"], out[invar])
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return poisson
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self.add_equation("poisson", poisson_compute_func)

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