deep-learning-pinn/collocation.py at main · runehoejlund/deep-learning-pinn · GitHub

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import torch
import lhsmdu

def uniform(n, n_i, n_b, _u_i, _u_b, xmin, xmax, tmin=0, tmax=1):
    n_x = n # Number of data points in interior domain
    n_t = n

    # Interior of domain
    X = torch.linspace(xmin, xmax, n_x+2, requires_grad=True)[1:-1]
    T = torch.linspace(tmin, tmax, n_t+1, requires_grad=True)[1:]
    x, t = torch.meshgrid((X, T))
    x = x.reshape(-1, 1)
    t = t.reshape(-1, 1)

    # Initial
    x_i = torch.linspace(xmin, xmax, n_i,requires_grad=True).reshape(-1, 1)[1:-1]
    t_i = torch.zeros(x_i.shape, requires_grad=True)
    u_i = _u_i(x_i)

    # Boundary
    x_b = torch.cat((xmin*torch.ones(n_b, 1, requires_grad=True),
                     xmax*torch.ones(n_b, 1, requires_grad=True)))
    t_b = torch.cat((torch.linspace(tmin, tmax, n_b, requires_grad=True),
                    torch.linspace(tmin, tmax, n_b, requires_grad=True)), dim=0).reshape(-1, 1)
    u_b = _u_b(t_b)

    return x, t, x_i, t_i, u_i, x_b, t_b, u_b

def hypercube(n, n_i, n_b, _u_i, _u_b, xmin, xmax, tmin=0, tmax=1):
    cube = lhsmdu.sample(2, n*n)
    x = torch.from_numpy(cube[0, :]).reshape(-1, 1)*(xmax-xmin) + xmin
    t = torch.from_numpy(cube[1, :]).reshape(-1, 1)*(tmax-tmin) + tmin
    x = x.float()
    t = t.float()
    x.requires_grad = True
    t.requires_grad = True

    # initial
    x_i = torch.from_numpy(lhsmdu.sample(1, n_i)[0, :]).reshape(-1, 1)*(xmax - xmin) + xmin
    x_i.requires_grad=True
    x_i = x_i.float()
    t_i = torch.ones(x_i.shape, requires_grad=True)*tmin
    u_i = _u_i(x_i)

    # boundary
    x_b = torch.cat((xmin*torch.ones(n_b, 1, requires_grad=True),
                     xmax*torch.ones(n_b, 1, requires_grad=True))).float()
    t_b = torch.from_numpy(lhsmdu.sample(1, n_b)[0, :]).reshape(-1, 1)*(tmax-tmin) + tmin
    t_b = torch.cat((t_b, t_b)).float()
    t_b.requires_grad=True
    u_b = _u_b(t_b)

    return x, t, x_i, t_i, u_i, x_b, t_b, u_b

def random(n, n_i, n_b, _u_i, _u_b, xmin, xmax, tmin=0, tmax=1):
    cube = lhsmdu.createRandomStandardUniformMatrix(2, n*n)
    x = torch.from_numpy(cube[0, :]).reshape(-1, 1)*(xmax-xmin) + xmin
    t = torch.from_numpy(cube[1, :]).reshape(-1, 1)*(tmax-tmin) + tmin
    x = x.float()
    t = t.float()
    x.requires_grad = True
    t.requires_grad = True

    # initial
    x_i = torch.from_numpy(lhsmdu.createRandomStandardUniformMatrix(1, n_i)[0, :]).reshape(-1, 1)*(xmax - xmin) + xmin
    x_i.requires_grad=True
    x_i = x_i.float()
    t_i = torch.zeros(x_i.shape, requires_grad=True)
    u_i = _u_i(x_i)

    # boundary
    x_b = torch.cat((xmin*torch.ones(n_b, 1, requires_grad=True),
                     xmax*torch.ones(n_b, 1, requires_grad=True))).float()
    t_b = torch.from_numpy(lhsmdu.createRandomStandardUniformMatrix(1, n_b)[0, :]).reshape(-1, 1)*(tmax-tmin) + tmin
    t_b = torch.cat((t_b, t_b)).float()
    t_b.requires_grad=True
    u_b = _u_b(t_b)

    return x, t, x_i, t_i, u_i, x_b, t_b, u_b