Surrogate pinns

examples/aneurysm/aneurysm_flow.py

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+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Reference: https://github.com/Jianxun-Wang/LabelFree-DNN-Surrogate
+"""
+
+import math
+import os
+import os.path as osp
+
+import matplotlib.pyplot as plt
+import numpy as np
+import paddle
+
+import ppsci
+from ppsci.utils import config
+from ppsci.utils import logger
+from ppsci.utils import misc
+
+if __name__ == "__main__":
+    args = config.parse_args()
+    paddle.fluid.core.set_prim_eager_enabled(True)
+
+    # set random seed for reproducibility
+    ppsci.utils.misc.set_random_seed(42)
+
+    # set output directory
+    OUTPUT_DIR = "./output_aneurysm_flow"
+
+    # initialize logger
+    logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info")
+
+    # Physic properties
+    P_OUT = 0  # pressure at the outlet of pipe
+    P_IN = 0.1  # pressure at the inlet of pipe
+    NU = 1e-3
+    RHO = 1
+
+    # Geometry
+    L = 1
+    X_IN = 0
+    X_OUT = X_IN + L
+    R_INLET = 0.05
+    mu = 0.5 * (X_OUT - X_IN)
+    N_Y = 20
+
+    x_inital = np.linspace(X_IN, X_OUT, 100, dtype=paddle.get_default_dtype()).reshape(
+        100, 1
+    )
+    x_20_copy = np.tile(x_inital, (20, 1))  # duplicate 20 times of x for dataloader
+
+    SIGMA = 0.1
+    SCALE_START = -0.02
+    SCALE_END = 0
+
+    scale_initial = np.linspace(
+        SCALE_START, SCALE_END, 50, endpoint=True, dtype=paddle.get_default_dtype()
+    ).reshape(50, 1)
+    scale = np.tile(scale_initial, (len(x_20_copy), 1))
+    x = np.array([np.tile(val, len(scale_initial)) for val in x_20_copy]).reshape(
+        len(scale), 1
+    )
+
+    # Axisymetric boundary
+    r_func = (
+        scale
+        / math.sqrt(2 * np.pi * SIGMA**2)
+        * np.exp(-((x - mu) ** 2) / (2 * SIGMA**2))
+    )
+
+    # Visualize stenosis(scale == 0.2)
+    PLOT_DIR = osp.join(OUTPUT_DIR, "visu")
+    os.makedirs(PLOT_DIR, exist_ok=True)
+    y_up = (R_INLET - r_func) * np.ones_like(x)
+    y_down = (-R_INLET + r_func) * np.ones_like(x)
+    idx = np.where(scale == 0)  # plot vessel which scale is 0.2 by finding its indexs
+    plt.figure()
+    plt.scatter(x[idx], y_up[idx])
+    plt.scatter(x[idx], y_down[idx])
+    plt.axis("equal")
+    plt.savefig(osp.join(PLOT_DIR, "idealized_stenotid_vessel"), bbox_inches="tight")
+
+    # Points and shuffle(for alignment)
+    y = np.zeros([len(x), 1], dtype=paddle.get_default_dtype())
+    for x0 in x_inital:
+        index = np.where(x[:, 0] == x0)[0]
+        # y is linear to scale, so we place linespace to get 1000 x, it coressponds to vessels
+        y[index] = np.linspace(
+            -max(y_up[index]),
+            max(y_up[index]),
+            len(index),
+            dtype=paddle.get_default_dtype(),
+        ).reshape(len(index), -1)
+
+    idx = np.where(scale == 0)  # plot vessel which scale is 0.2 by finding its indexs
+    plt.figure()
+    plt.scatter(x[idx], y[idx])
+    plt.axis("equal")
+    plt.savefig(osp.join(PLOT_DIR, "one_scale_sample"), bbox_inches="tight")
+
+    interior_geom = ppsci.geometry.PointCloud(
+        interior={"x": x, "y": y, "scale": scale},
+        coord_keys=("x", "y", "scale"),
+    )
+    geom = {"interior": interior_geom}
+
+    def init_func(m):
+        if misc.typename(m) == "Linear":
+            ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True)
+
+    model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u",), 3, 20, "silu")
+    model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v",), 3, 20, "silu")
+    model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p",), 3, 20, "silu")
+    model_1.apply(init_func)
+    model_2.apply(init_func)
+    model_3.apply(init_func)
+
+    class Transform:
+        def __init__(self) -> None:
+            pass
+
+        def input_trans(self, input):
+            self.input = input
+            return input
+
+        def output_trans_u(self, out):
+            x, y, scale = self.input["x"], self.input["y"], self.input["scale"]
+            r_func = (
+                scale
+                / np.sqrt(2 * np.pi * SIGMA**2)
+                * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2))
+            )
+            self.h = R_INLET - r_func
+            u = out["u"]
+            # The no-slip condition of velocity on the wall
+            return {"u": u * (self.h**2 - y**2)}
+
+        def output_trans_v(self, out):
+            y = self.input["y"]
+            v = out["v"]
+            # The no-slip condition of velocity on the wall
+            return {"v": (self.h**2 - y**2) * v}
+
+        def output_trans_p(self, out):
+            x = self.input["x"]
+            p = out["p"]
+            # The pressure inlet [p_in = 0.1] and outlet [p_out = 0]
+            return {
+                "p": ((P_IN - P_OUT) * (X_OUT - x) / L + (X_IN - x) * (X_OUT - x) * p)
+            }
+
+    transform = Transform()
+    model_1.register_input_transform(transform.input_trans)
+    model_2.register_input_transform(transform.input_trans)
+    model_3.register_input_transform(transform.input_trans)
+    model_1.register_output_transform(transform.output_trans_u)
+    model_2.register_output_transform(transform.output_trans_v)
+    model_3.register_output_transform(transform.output_trans_p)
+    model = ppsci.arch.ModelList((model_1, model_2, model_3))
+
+    LEARNING_RATE = 1e-3
+
+    optimizer_1 = ppsci.optimizer.Adam(
+        LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15
+    )((model_1,))
+    optimizer_2 = ppsci.optimizer.Adam(
+        LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15
+    )((model_2,))
+    optimizer_3 = ppsci.optimizer.Adam(
+        LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15
+    )((model_3,))
+    optimizer = ppsci.optimizer.OptimizerList((optimizer_1, optimizer_2, optimizer_3))
+
+    equation = {"NavierStokes": ppsci.equation.NavierStokes(NU, RHO, 2, False)}
+
+    BATCH_SIZE = 50
+
+    pde_constraint = ppsci.constraint.InteriorConstraint(
+        equation["NavierStokes"].equations,
+        {"continuity": 0, "momentum_x": 0, "momentum_y": 0},
+        geom=geom["interior"],
+        dataloader_cfg={
+            "dataset": "NamedArrayDataset",
+            "num_workers": 1,
+            "batch_size": BATCH_SIZE,
+            "iters_per_epoch": int(x.shape[0] / BATCH_SIZE),
+            "sampler": {
+                "name": "BatchSampler",
+                "shuffle": True,
+                "drop_last": False,
+            },
+        },
+        loss=ppsci.loss.MSELoss("mean"),
+        evenly=True,
+        name="EQ",
+    )
+    constraint = {pde_constraint.name: pde_constraint}
+
+    EPOCHS = 400 if not args.epochs else args.epochs
+
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model,
+        constraint,
+        OUTPUT_DIR,
+        optimizer,
+        epochs=EPOCHS,
+        iters_per_epoch=int(x.shape[0] / BATCH_SIZE),
+        save_freq=10,
+        equation=equation,
+    )
+
+    solver.train()
+
+    def model_predict(
+        x: np.ndarray, y: np.ndarray, scale: np.ndarray, solver: ppsci.solver.Solver
+    ):
+        xt = paddle.to_tensor(x)
+        yt = paddle.to_tensor(y)
+        scalet = paddle.full_like(xt, scale)
+        input_dict = {"x": xt, "y": yt, "scale": scalet}
+        output_dict = solver.predict(input_dict, batch_size=100)
+        return {k: v.numpy() for k, v in output_dict.items()}
+
+    scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[
+        "scale"
+    ]
+    CASE_SELECTED = [1, 151, 486]
+    PLOT_X = 0.8
+    PLOT_Y = 0.06
+    FONTSIZE = 14
+    axis_limit = [0, 1, -0.15, 0.15]
+    path = "./data/cases/"
+    D_P = 0.1
+    error_u = []
+    error_v = []
+    N_CL = 200  # number of sampling points in centerline (confused about centerline, but the paper did not explain)
+    x_centerline = np.linspace(
+        X_IN, X_OUT, N_CL, dtype=paddle.get_default_dtype()
+    ).reshape(N_CL, 1)
+    y_centerline = np.zeros_like(x_centerline)
+    for case_id in CASE_SELECTED:
+        scale = scale_test[case_id - 1]
+        data_CFD = np.load(osp.join(path, f"{case_id}CFD_contour.npz"))
+        x = data_CFD["x"].astype(paddle.get_default_dtype())
+        y = data_CFD["y"].astype(paddle.get_default_dtype())
+        u_cfd = data_CFD["U"].astype(paddle.get_default_dtype())
+        # p_cfd = data_CFD["P"].astype(paddle.get_default_dtype()) # missing data
+
+        n = len(x)
+        output_dict = model_predict(
+            x.reshape(n, 1),
+            y.reshape(n, 1),
+            np.full((n, 1), scale, dtype=paddle.get_default_dtype()),
+            solver,
+        )
+        u, v, p = (
+            output_dict["u"],
+            output_dict["v"],
+            output_dict["p"],
+        )
+        w = np.zeros_like(u)
+        u_vec = np.concatenate([u, v, w], axis=1)
+        error_u.append(
+            np.linalg.norm(u_vec[:, 0] - u_cfd[:, 0]) / (D_P * len(u_vec[:, 0]))
+        )
+        error_v.append(
+            np.linalg.norm(u_vec[:, 1] - u_cfd[:, 1]) / (D_P * len(u_vec[:, 0]))
+        )
+        # error_p = np.linalg.norm(p - p_cfd) / (D_P * D_P)
+
+        # Streamwise velocity component u
+        plt.figure()
+        plt.subplot(212)
+        plt.scatter(x, y, c=u_vec[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0]))
+        plt.text(PLOT_X, PLOT_Y, r"DNN", {"color": "b", "fontsize": FONTSIZE})
+        plt.axis(axis_limit)
+        plt.colorbar()
+        plt.subplot(211)
+        plt.scatter(x, y, c=u_cfd[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0]))
+        plt.colorbar()
+        plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE})
+        plt.axis(axis_limit)
+        plt.savefig(
+            osp.join(PLOT_DIR, f"{case_id}_scale_{scale}_uContour_test.png"),
+            bbox_inches="tight",
+        )
+
+        # Spanwise velocity component v
+        plt.figure()
+        plt.subplot(212)
+        plt.scatter(x, y, c=u_vec[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1]))
+        plt.text(PLOT_X, PLOT_Y, r"DNN", {"color": "b", "fontsize": FONTSIZE})
+        plt.axis(axis_limit)
+        plt.colorbar()
+        plt.subplot(211)
+        plt.scatter(x, y, c=u_cfd[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1]))
+        plt.colorbar()
+        plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE})
+        plt.axis(axis_limit)
+        plt.savefig(
+            osp.join(PLOT_DIR, f"{case_id}_scale_{scale}_vContour_test.png"),
+            bbox_inches="tight",
+        )
+        plt.close("all")
+
+        # Centerline wall shear profile tau_c (downside)
+        data_CFD_wss = np.load(osp.join(path, f"{case_id}CFD_wss.npz"))
+        x_inital = data_CFD_wss["x"]
+        wall_shear_mag_up = data_CFD_wss["wss"]
+
+        D_H = 0.001  # The spanwise distance is approximately the height of the wall
+        r_cl = (
+            scale
+            / np.sqrt(2 * np.pi * SIGMA**2)
+            * np.exp(-((x_centerline - mu) ** 2) / (2 * SIGMA**2))
+        )
+        y_wall = (-R_INLET + D_H) * np.ones_like(x_centerline) + r_cl
+        output_dict_wss = model_predict(
+            x_centerline,
+            y_wall,
+            np.full((N_CL, 1), scale, dtype=paddle.get_default_dtype()),
+            solver,
+        )
+        v_cl_total = np.zeros_like(
+            x_centerline
+        )  # assuming normal velocity along the wall is zero
+        u_cl = output_dict_wss["u"]
+        v_cl = output_dict_wss["v"]
+        v_cl_total = np.sqrt(u_cl**2 + v_cl**2)
+        tau_c = NU * v_cl_total / D_H
+
+        plt.figure()
+        plt.plot(
+            x_inital,
+            wall_shear_mag_up,
+            label="CFD",
+            color="darkblue",
+            linestyle="-",
+            lw=3.0,
+            alpha=1.0,
+        )
+        plt.plot(
+            x_inital,
+            tau_c,
+            label="DNN",
+            color="red",
+            linestyle="--",
+            dashes=(5, 5),
+            lw=2.0,
+            alpha=1.0,
+        )
+        plt.xlabel(r"x", fontsize=16)
+        plt.ylabel(r"$\tau_{c}$", fontsize=16)
+        plt.legend(prop={"size": 16})
+        plt.savefig(
+            osp.join(PLOT_DIR, f"{case_id}_nu__{scale}_wallshear_test.png"),
+            bbox_inches="tight",
+        )
+        plt.close("all")
+    logger.info(
+        f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}"
+    )
+    logger.info(
+        f"Table 1 : Aneurysm - Geometry error v : {sum(error_v) / len(error_v): .3e}"
+    )