def PDE_loss(x, psi, E):
psi_dx = dfx(x, psi)
psi_ddx = dfx(x, psi_dx[:, 0])[:, 0] + dfx(x, psi_dx[:, 1])[:, 1] + dfx(
x, psi_dx[:, 2])[:, 2]
diff = (prefactor_laplacian * psi_ddx + (potential(x) - E) * psi)
loss = torch.mean(diff**2)
return loss
def PDE_loss(x, psi, E):
psi_dx = dfx(x, psi)
psi_ddx = dfx(x, psi_dx[:, 0])[:, 0] + dfx(x, psi_dx[:, 1])[:, 1]
diff = 1 / 2 * psi_ddx + (E - potential(
x)) * psi # -hbar**2/(2*m_e) * psi_ddx + potential(x) * psi - E * psi
loss = torch.mean(diff**2)
return loss
def PDE_loss(x, psi, E):
psi_dx = dfx(x, psi)
psi_ddx = dfx(x, psi_dx[:, 0])[:, 0] + dfx(x, psi_dx[:, 1])[:, 1] + dfx(
x, psi_dx[:, 2])[:, 2]
r = torch.sqrt(torch.sum(x**2, dim=1))
diff = laplacian_prefactor * psi_ddx + 1 / r * psi + energy_prefactor * E * psi
loss = torch.mean(diff**2)
return loss
def PDE_loss(x, psi, E):
psi_dx = dfx(x, psi)
psi_ddx = dfx(x, psi_dx)
diff = 1 / 2 * psi_ddx + E * psi # -hbar**2/(2*m_e) * psi_ddx + potential(x) * psi - E * psi
loss = torch.mean(diff**2)
return loss