二维&三维Poisson问题
本案例要求MindSpore版本 >= 2.0.0调用如下接口: mindspore.jit，mindspore.jit_class，mindspore.jacrev。
问题描述
本案例演示如何利用PINNs在不同几何体下求解二维和三维泊松方程。二维泊松方程定义为
\[\Delta u = -\sin(4\pi x)\sin(4\pi y),\]
而三维方程定义为
\[\Delta u = -\sin(4\pi x)\sin(4\pi y)\sin(4\pi z),\]
很容易验证，以下函数分别满足二维和三维泊松方程
\[\begin{split}u = \frac{1}{32\pi^2} \sin(4\pi x)\sin(4\pi y), \\
u = \frac{1}{48\pi^2} \sin(4\pi x)\sin(4\pi y)\sin(4\pi z).\end{split}\]
如果在几何体边界按以上函数取狄利克雷边界条件，那么这些函数就是我们想要得到的解。因而，我们可以利用以上函数来验证结果。 对于二维问题，本例演示在矩形，圆形，三角形和五边形区域求解方程，而对于三维问题，我们将在四面体，圆柱和圆锥区域内求解方程。
技术路径
MindSpore Flow求解该问题的具体流程如下：
创建数据集。
构建模型。
优化器。
Poisson。
模型训练。
模型评估。
下述poisson_cfg.yaml配置文件可以在applications/physics_driven/poisson/point_source/poisson_cfg.yaml下载, src包可以在applications/physics_driven/poisson/point_source/src下载。
[1]:
import time
import mindspore as ms
from mindspore import nn, ops, jit
from mindflow import load_yaml_config
from src.model import create_model
from src.lr_scheduler import OneCycleLR
from src.dataset import create_dataset
ms.set_context(mode=ms.GRAPH_MODE, save_graphs=False, device_target="GPU")
# Load config
file_cfg = "poisson_cfg.yaml"
config = load_yaml_config(file_cfg)
创建数据集
本案例在求解域及边值条件进行随机采样，生成训练数据集与测试数据集。具体方法见src/dataset.py。设值geom_name来选择几何体，可选择rectangle、disk、triangle、pentagon、tetrahedon、cylinder和cone。
[2]:
geom_name = "triangle"
ds_train, n_dim = create_dataset(geom_name, config)
构建模型
本案例采用带3个隐藏层的多层感知器，并带有以下特点:
采用激活函数： \(f(x) = x \exp(-x^2/(2e))\)
最后一层线性层使用weight normalization。
所有权重都采用
mindspore的HeUniform初始化。
具体定义见src/model.py。
[3]:
model = create_model(**config['model'][f'{n_dim}d'])
Poisson
在利用mindflow求解PDE时，我们需要写一个mindflow.PDEWithLloss的子类来定义控制方程，边界条件和损失函数。在求解区域内和边界上均采用L2损失，并利用mindflow的MTLWeightedLossCell多目标损失函数将两个损失结合起来。
[4]:
import sympy
from mindspore import numpy as ms_np
from mindflow import PDEWithLoss, MTLWeightedLossCell, sympy_to_mindspore
class Poisson(PDEWithLoss):
"""Define the loss of the Poisson equation."""
def __init__(self, model, n_dim):
if n_dim == 2:
var_str = 'x y'
elif n_dim == 3:
var_str = 'x y z'
else:
raise ValueError("`n_dim` can only be 2 or 3.")
self.in_vars = sympy.symbols(var_str)
self.out_vars = (sympy.Function('u')(*self.in_vars),)
super(Poisson, self).__init__(model, self.in_vars, self.out_vars)
self.bc_nodes = sympy_to_mindspore(self.bc(n_dim), self.in_vars, self.out_vars)
self.loss_fn = MTLWeightedLossCell(num_losses=2)
def pde(self):
"""Define the gonvering equation."""
poisson = 0
src_term = 1
sym_u = self.out_vars[0]
for var in self.in_vars:
poisson += sympy.diff(sym_u, (var, 2))
src_term *= sympy.sin(4*sympy.pi*var)
poisson += src_term
equations = {"poisson": poisson}
return equations
def bc(self, n_dim):
"""Define the boundary condition."""
bc_term = 1
for var in self.in_vars:
bc_term *= sympy.sin(4*sympy.pi*var)
bc_term *= 1/(16*n_dim*sympy.pi*sympy.pi)
bc_eq = self.out_vars[0] - bc_term
equations = {"bc": bc_eq}
return equations
def get_loss(self, pde_data, bc_data):
"""Define the loss function."""
res_pde = self.parse_node(self.pde_nodes, inputs=pde_data)
res_bc = self.parse_node(self.bc_nodes, inputs=bc_data)
loss_pde = ms_np.mean(ms_np.square(res_pde[0]))
loss_bc = ms_np.mean(ms_np.square(res_bc[0]))
return self.loss_fn((loss_pde, loss_bc))
# Create the problem
problem = Poisson(model, n_dim)
poisson: sin(4*pi*x)*sin(4*pi*y) + Derivative(u(x, y), (x, 2)) + Derivative(u(x, y), (y, 2))
Item numbers of current derivative formula nodes: 3
bc: u(x, y) - sin(4*pi*x)*sin(4*pi*y)/(32*pi**2)
Item numbers of current derivative formula nodes: 2
优化器
本案例采用Adam优化器，并配合Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates提出的动态学习率进行训练。动态学习率定义参见src/lr_scheduler.py。
[5]:
n_epochs = 50
params = model.trainable_params() + problem.loss_fn.trainable_params()
steps_per_epoch = config['data']['domain']['size']//config['batch_size']
learning_rate = OneCycleLR(total_steps=steps_per_epoch*n_epochs, **config['optimizer'])
opt = nn.Adam(params, learning_rate=learning_rate)
模型训练
使用MindSpore>= 2.0.0的版本，可以使用函数式编程范式训练神经网络。
[6]:
def train():
# Create
grad_fn = ms.value_and_grad(problem.get_loss, None, opt.parameters, has_aux=False)
@jit
def train_step(pde_data, bc_data):
loss, grads = grad_fn(pde_data, bc_data)
loss = ops.depend(loss, opt(grads))
return loss
def train_epoch(model, dataset, i_epoch):
n_step = dataset.get_dataset_size()
model.set_train()
for i_step, (pde_data, bc_data) in enumerate(dataset):
local_time_beg = time.time()
loss = train_step(pde_data, bc_data)
if i_step%50 == 0 or i_step + 1 == n_step:
print("\repoch: {}, loss: {:>f}, time elapsed: {:.1f}ms [{}/{}]".format(
i_epoch, float(loss), (time.time() - local_time_beg)*1000, i_step + 1, n_step))
for i_epoch in range(n_epochs):
train_epoch(model, ds_train, i_epoch)
[7]:
time_beg = time.time()
train()
print("End-to-End total time: {} s".format(time.time() - time_beg))
epoch: 0, loss: 1.527029, time elapsed: 12050.1ms [1/200]
epoch: 0, loss: 1.468655, time elapsed: 52.4ms [51/200]
epoch: 0, loss: 1.442717, time elapsed: 52.3ms [101/200]
epoch: 0, loss: 1.430150, time elapsed: 52.4ms [151/200]
epoch: 0, loss: 1.420228, time elapsed: 53.4ms [200/200]
epoch: 1, loss: 1.419910, time elapsed: 53.0ms [1/200]
epoch: 1, loss: 1.407040, time elapsed: 52.5ms [51/200]
epoch: 1, loss: 1.386505, time elapsed: 52.4ms [101/200]
epoch: 1, loss: 1.362307, time elapsed: 52.4ms [151/200]
epoch: 1, loss: 1.349054, time elapsed: 52.5ms [200/200]
epoch: 2, loss: 1.349143, time elapsed: 53.7ms [1/200]
epoch: 2, loss: 1.336657, time elapsed: 52.7ms [51/200]
epoch: 2, loss: 1.323158, time elapsed: 52.6ms [101/200]
epoch: 2, loss: 1.307419, time elapsed: 52.9ms [151/200]
epoch: 2, loss: 1.289993, time elapsed: 52.7ms [200/200]
epoch: 3, loss: 1.289594, time elapsed: 53.5ms [1/200]
epoch: 3, loss: 1.270476, time elapsed: 52.4ms [51/200]
epoch: 3, loss: 1.246817, time elapsed: 52.6ms [101/200]
epoch: 3, loss: 1.222093, time elapsed: 52.6ms [151/200]
epoch: 3, loss: 1.194862, time elapsed: 52.3ms [200/200]
epoch: 4, loss: 1.194533, time elapsed: 52.5ms [1/200]
epoch: 4, loss: 1.164445, time elapsed: 52.6ms [51/200]
epoch: 4, loss: 1.134136, time elapsed: 52.5ms [101/200]
epoch: 4, loss: 1.100014, time elapsed: 52.6ms [151/200]
epoch: 4, loss: 1.064941, time elapsed: 52.4ms [200/200]
...
epoch: 45, loss: 0.001281, time elapsed: 53.0ms [1/200]
epoch: 45, loss: 0.001264, time elapsed: 52.6ms [51/200]
epoch: 45, loss: 0.001263, time elapsed: 52.5ms [101/200]
epoch: 45, loss: 0.001236, time elapsed: 52.6ms [151/200]
epoch: 45, loss: 0.001237, time elapsed: 52.5ms [200/200]
epoch: 46, loss: 0.001218, time elapsed: 52.7ms [1/200]
epoch: 46, loss: 0.001209, time elapsed: 52.6ms [51/200]
epoch: 46, loss: 0.001191, time elapsed: 52.6ms [101/200]
epoch: 46, loss: 0.001202, time elapsed: 52.7ms [151/200]
epoch: 46, loss: 0.001182, time elapsed: 52.9ms [200/200]
epoch: 47, loss: 0.001174, time elapsed: 53.0ms [1/200]
epoch: 47, loss: 0.001186, time elapsed: 52.7ms [51/200]
epoch: 47, loss: 0.001182, time elapsed: 52.6ms [101/200]
epoch: 47, loss: 0.001169, time elapsed: 52.8ms [151/200]
epoch: 47, loss: 0.001172, time elapsed: 52.7ms [200/200]
epoch: 48, loss: 0.001165, time elapsed: 52.7ms [1/200]
epoch: 48, loss: 0.001168, time elapsed: 52.6ms [51/200]
epoch: 48, loss: 0.001148, time elapsed: 52.5ms [101/200]
epoch: 48, loss: 0.001159, time elapsed: 52.7ms [151/200]
epoch: 48, loss: 0.001171, time elapsed: 52.5ms [200/200]
epoch: 49, loss: 0.001156, time elapsed: 52.7ms [1/200]
epoch: 49, loss: 0.001155, time elapsed: 52.6ms [51/200]
epoch: 49, loss: 0.001148, time elapsed: 52.6ms [101/200]
epoch: 49, loss: 0.001159, time elapsed: 52.9ms [151/200]
epoch: 49, loss: 0.001153, time elapsed: 52.6ms [200/200]
End-to-End total time: 584.182409286499 s
模型评估
可通过以下函数来计算模型的L2相对误差。
[8]:
from eval import calculate_l2_error
n_samps = 5000 # Number of test samples
ds_test, _ = create_dataset(geom_name, config, n_samps)
calculate_l2_error(model, ds_test, n_dim)
Relative L2 error (domain): 0.0310
Relative L2 error (bc): 0.0833