想复现一下这个项目https://github.com/DENG-MIT/Stiff-PINN ，利用anaconda和pycharm安装相关库后调试无法顺利运行，报错信息只有Could not find GLIMDA.找了两三天也没找到有用的解决方案。
import cantera as ct
import matplotlib.pyplot as plt
import numpy as np
import torch
from config import device
from assimulo.solvers import GLIMDA
from assimulo.problem import Implicit_Problem
torch.set_default_tensor_type("torch.DoubleTensor")
def to_np(y):
return y.detach().cpu().numpy()
def get_idt(gas, P, composition, T, energy='on'):
gas.TPX = T, P, composition
r = ct.IdealGasConstPressureReactor(gas, energy=energy)
sim = ct.ReactorNet([r])
idt = 0
t_end = 1e-0
states = ct.SolutionArray(gas, extra=["t"])
while sim.time < t_end:
sim.step()
states.append(r.thermo.state, t=sim.time)
# Ignition creterior: temperature or species
if energy == 'on':
if r.thermo.T > T + 600 and idt < 1e-10:
idt = sim.time
else:
# index 0 is the species O2 (Oxygen)
if r.thermo.Y[0] < 1e-3 and idt < 1e-10:
idt = sim.time
if idt > 1e-10 and sim.time > 2 * idt:
break
if idt < 1e-10:
idt = t_end
states_np = np.hstack((np.atleast_2d(states.T).T, states.Y))
t_tensor = torch.Tensor(states.t).view(-1, 1).to(device=device)
TY_tensor = torch.Tensor(states_np).to(device=device)
return idt, t_tensor, TY_tensor
def grad_norm(pinn_model):
total_norm = 0
for p in pinn_model.parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm ** (1. / 2)
return total_norm
def grad_mean(pinn_model):
total_norm = 0
count = 0
for p in pinn_model.parameters():
param_norm = p.grad.data.norm(1)
total_norm += param_norm.item()
count += torch.numel(p.grad.data)
grad_mean = total_norm / count
return grad_mean
def plot_pinn_y(x_pinn, y_pinn, t_true, y_true,
dydt, rhs, loss_list, x_scale):
species_names = np.array(['0', '1', '3', '6', '7', '8', '11', '14', '16', '17'])
species_names_int = np.array([0, 1, 3, 6, 7, 8, 11, 14, 16, 17])
fig = plt.figure(figsize=(27, 24))
for i in range(len(species_names)):
i_species = i
ax = fig.add_subplot(4, 6, i + 1)
ax.plot(x_pinn / x_scale,
y_pinn[:, i_species],
color="r",
ls="-",
label='pinn')
ax.plot(t_true / x_scale,
y_true[:, species_names_int[i]],
color="b",
ls="-",
marker=None,
fillstyle='none',
markersize=6,
alpha=1.0,
label='true')
#ax.set_xlim(left=1e-2)
#ax.set_xscale('log')
ax2 = ax.twinx()
ax2.plot(x_pinn / x_scale,
dydt[:, i_species],
ls='--',
color='r',
alpha=0.8,
label='dydt')
ax2.plot(x_pinn / x_scale,
rhs[:, i_species],
ls='--',
color='b',
alpha=0.8,
label='rhs')
ax.set_title(species_names[i])
if i == 0:
ax.legend(loc='upper left', shadow=False, framealpha=0.5)
if i == 1:
ax2.legend(loc='upper right', shadow=False, framealpha=0.5)
# plot loss
key_list_loss = ['res_train', 'res_test']
ax = fig.add_subplot(4, 6, i + 2)
epoch = len(loss_list['res_train'])
for key in key_list_loss:
ax.plot(np.arange(epoch) + 1, loss_list[key], '-', lw=2, label=key)
ax.legend(loc='upper right', shadow=False, framealpha=0.3)
ax.set_yscale("log")
if epoch > 2000:
ax.set_xscale("log")
ax.set_xlabel('Epoch')
ax.set_ylabel('Loss')
key_list_loss2 = ['res_0', 'res_1', 'res_3', 'res_6', 'res_7',
'res_8', 'res_11', 'res_14', 'res_16', 'res_17']
ax = fig.add_subplot(4, 6, i + 3)
epoch = len(loss_list['res_train'])
for key in key_list_loss2:
ax.plot(np.arange(epoch) + 1, loss_list[key], '-', lw=2, label=key)
ax.legend(loc='upper right', shadow=False, framealpha=0.3)
ax.set_yscale("log")
if epoch > 2000:
ax.set_xscale("log")
ax.set_xlabel('Epoch')
ax.set_ylabel('Loss')
key_list_loss3 = ['grad_norm']
ax = fig.add_subplot(4, 6, i + 4)
epoch = len(loss_list['grad_norm'])
for key in key_list_loss3:
ax.plot(np.arange(epoch) + 1, loss_list[key], '-', lw=2, label=key)
ax.legend(loc='upper right', shadow=False, framealpha=0.3)
ax.set_yscale("log")
if epoch > 2000:
ax.set_xscale("log")
ax.set_xlabel('Epoch')
ax.set_ylabel('Grad norm')
key_list_loss4 = ['slope']
ax = fig.add_subplot(4, 6, i + 5)
epoch = len(loss_list['slope'])
for key in key_list_loss4:
ax.plot(np.arange(epoch) + 1, loss_list[key], '-', lw=2, label=key)
ax.legend(loc='upper right', shadow=False, framealpha=0.3)
ax.set_yscale("log")
if epoch > 2000:
ax.set_xscale("log")
ax.set_xlabel('Epoch')
ax.set_ylabel('Slope')
fig.tight_layout()
# plt.show()
figname = 'figs/pinn_loss.png'
fig.savefig(figname, dpi=120, facecolor='w', edgecolor='w', orientation='portrait')
ax.cla()
fig.clf()
plt.close()
Could not find GLIMDA.
进程已结束,退出代码0
