bte.grad.GradEquation

Module Contents

Classes

Equation_HermiteBased

Base class for all neural network modules.

Functions

MaxRootOfHermitePolynomial(N)
simpson(l, m, r)
linear_reconstruction(dis, disL, disR[, limiter])

Attributes

Tensor

bte.grad.GradEquation.Tensor

bte.grad.GradEquation.MaxRootOfHermitePolynomial(N)

Parameters:

N (int) –

Return type:

float

bte.grad.GradEquation.simpson(l, m, r)

bte.grad.GradEquation.linear_reconstruction(dis, disL, disR, limiter=limiter_mc)

Parameters:

• dis (bte.grad.distribution.HermitedistributionBase) –

• disL (bte.grad.distribution.HermitedistributionBase) –

• disR (bte.grad.distribution.HermitedistributionBase) –

class bte.grad.GradEquation.Equation_HermiteBased(M=5, bdc='circular')

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

set_collision(colltype)

Parameters:

colltype (str) –

set_QKernel()

__str__()

Return str(self).

Return type:

str

set_order(space_order, time_order)

set_space_order(order)

set_time_order(order)

set_initial(kn, f0, dx)

Parameters:

• kn (bte.utils.indexs.torch.Tensor) –

• f0 (bte.grad.distribution.HermitedistributionBase) –

• dx (float) –

Grad_Vec(dis, Kn=None)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

Grad_Vec_LLF(dis, Kn=None)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

Grad_Closure(dis, Kn=None)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

NRxx_Closure(dis, Kn)

Parameters:

• dis (bte.grad.distribution.HermitedistributionBase) –

• Kn (Tensor) –

Return type:

Tensor

getTau(dis, Kn)

collision_BGK(dis, dt, Kn)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

collision_Kernel(dis, dt, Kn)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

get_tstep_cell(dis)

因为一般是在碰撞后求解下一步的时间步长，所以直接读取， 若非如此，需要 _,c,theta=get_pcs(dis)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

get_tstep(dislist, dx)

Parameters:

dislist (bte.grad.distribution.HermitedistributionBase) –

LLF_helper(disL, disR, fluxL, fluxR, lam)

Parameters:

• disL (bte.grad.distribution.HermitedistributionBase) –

• disR (bte.grad.distribution.HermitedistributionBase) –

• fluxL (bte.grad.distribution.HermitedistributionBase) –

• fluxR (bte.grad.distribution.HermitedistributionBase) –

HLL_helper(disL, disR, fluxL, fluxR, lambda_L, lambda_R)

Parameters:

• disL (bte.grad.distribution.HermitedistributionBase) –

• disR (bte.grad.distribution.HermitedistributionBase) –

• fluxL (bte.grad.distribution.HermitedistributionBase) –

• fluxR (bte.grad.distribution.HermitedistributionBase) –

Return type:

bte.grad.distribution.HermitedistributionBase

flux_helper(disL, disR, fluxL, fluxR, Kn, disLO, disRO, lam=0)

Parameters:

• disL (bte.grad.distribution.HermitedistributionBase) –

• disR (bte.grad.distribution.HermitedistributionBase) –

• fluxL (bte.grad.distribution.HermitedistributionBase) –

• fluxR (bte.grad.distribution.HermitedistributionBase) –

• disLO (bte.grad.distribution.HermitedistributionBase) –

• disRO (bte.grad.distribution.HermitedistributionBase) –

Return type:

bte.grad.distribution.HermitedistributionBase

get_flux_hme_helper(disL, disR)

Parameters:

• disL (bte.grad.distribution.HermitedistributionBase) –

• disR (bte.grad.distribution.HermitedistributionBase) –

get_flux_hme(dis, dx, Kn)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

padding_periodic_dis(f)

Parameters:

f (bte.grad.distribution.HermitedistributionBase) –

padding_replicate_dis(f)

Parameters:

f (bte.grad.distribution.HermitedistributionBase) –

padding_constant_dis(f)

Parameters:

f (bte.grad.distribution.HermitedistributionBase) –

padding_reflect_dis(f)

Parameters:

f (bte.grad.distribution.HermitedistributionBase) –

padding_periodic_single(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

padding_replicate_single(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

padding_reflect_single(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

padding_reflect_single_u(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

padding_constant_single(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

padding_dis(f)

Parameters:

f (bte.utils.indexs.torch.Tensor) –

Reconstruction_constant(dis)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

Reconstruction_linear(dis)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

abstract Reconstruction_WENO(disM)

Parameters:

disM (bte.grad.distribution.HermitedistributionBase) –

maxwellian_bdc_fix(disLP_R, disMP_L, disMP_R, disRP_L, disLO, disRO)

get_convection_flux(dis, dx, Kn)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

get_flux(dis, dx, Kn)

Euler(dis, dt, dx, Kn)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

Heun(dis, dt, dx, Kn)

apply_flux(dis, flux)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

forward(dis, T, dx, Kn, verbose=False)

Parameters:

dis (bte.grad.distribution.HermitedistributionBase) –

solve_to(T, verbose=False)

run(dis, T, dx, Kn=None, verbose=False)

set_BC_Value(LV, RV)