Modulus Sym Equations

Advection diffusion equation Reference: https://en.wikipedia.org/wiki/Convection%E2%80%93diffusion_equation

class modulus.sym.eq.pdes.advection_diffusion.AdvectionDiffusion(T='T', D='D', Q=0, rho='rho', dim=3, time=False, mixed_form=False)[source]

Bases: PDE

Advection diffusion equation

Parameters

• T (str) – The dependent variable.

• D (float, Sympy Symbol/Expr, str) – Diffusivity. If D is a str then it is converted to Sympy Function of form ‘D(x,y,z,t)’. If ‘D’ is a Sympy Symbol or Expression then this is substituted into the equation.

• Q (float, Sympy Symbol/Expr, str) – The source term. If Q is a str then it is converted to Sympy Function of form ‘Q(x,y,z,t)’. If ‘Q’ is a Sympy Symbol or Expression then this is substituted into the equation. Default is 0.

• rho (float, Sympy Symbol/Expr, str) – The density. If rho is a str then it is converted to Sympy Function of form ‘rho(x,y,z,t)’. If ‘rho’ is a Sympy Symbol or Expression then this is substituted into the equation to allow for compressible Navier Stokes.

• dim (int) – Dimension of the diffusion equation (1, 2, or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is False.

• mixed_form (bool) – If True, use the mixed formulation of the wave equation.

Examples

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>>> ad = AdvectionDiffusion(D=0.1, rho=1.)
>>> ad.pprint()
advection_diffusion: u*T__x + v*T__y + w*T__z - 0.1*T__x__x - 0.1*T__y__y - 0.1*T__z__z
>>> ad = AdvectionDiffusion(D='D', rho=1, dim=2, time=True)
>>> ad.pprint()
advection_diffusion: -D*T__x__x - D*T__y__y + u*T__x + v*T__y - D__x*T__x - D__y*T__y + T__t
        

Basic equations

class modulus.sym.eq.pdes.basic.Curl(vector, curl_name=['u', 'v', 'w'])[source]

Bases: PDE

del cross vector operator

Parameters

• vector (tuple of 3 Sympy Exprs, floats, ints or strings) – This will be the vector to take the curl of.

• curl_name (tuple of 3 strings) – These will be the output names of the curl operations.

Examples

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>>> c = Curl((0,0,'phi'), ('u','v','w'))
>>> c.pprint()
u: phi__y
v: -phi__x
w: 0
        

class modulus.sym.eq.pdes.basic.GradNormal(T, dim=3, time=True)[source]

Bases: PDE

Implementation of the gradient boundary condition

Parameters

• T (str) – The dependent variable.

• dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

Examples

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>>> gn = ns = GradNormal(T='T')
>>> gn.pprint()
normal_gradient_T: normal_x*T__x + normal_y*T__y + normal_z*T__z
        

class modulus.sym.eq.pdes.basic.NormalDotVec(vec=['u', 'v', 'w'])[source]

Bases: PDE

Normal dot velocity

Parameters

dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

Diffusion equation

class modulus.sym.eq.pdes.diffusion.Diffusion(T='T', D='D', Q=0, dim=3, time=True, mixed_form=False)[source]

Bases: PDE

Diffusion equation

Parameters

• T (str) – The dependent variable.

• D (float, Sympy Symbol/Expr, str) – Diffusivity. If D is a str then it is converted to Sympy Function of form ‘D(x,y,z,t)’. If ‘D’ is a Sympy Symbol or Expression then this is substituted into the equation.

• Q (float, Sympy Symbol/Expr, str) – The source term. If Q is a str then it is converted to Sympy Function of form ‘Q(x,y,z,t)’. If ‘Q’ is a Sympy Symbol or Expression then this is substituted into the equation. Default is 0.

• dim (int) – Dimension of the diffusion equation (1, 2, or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

• mixed_form (bool) – If True, use the mixed formulation of the diffusion equations.

Examples

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>>> diff = Diffusion(D=0.1, Q=1, dim=2)
>>> diff.pprint()
diffusion_T: T__t - 0.1*T__x__x - 0.1*T__y__y - 1
>>> diff = Diffusion(T='u', D='D', Q='Q', dim=3, time=False)
>>> diff.pprint()
diffusion_u: -D*u__x__x - D*u__y__y - D*u__z__z - Q - D__x*u__x - D__y*u__y - D__z*u__z
        

class modulus.sym.eq.pdes.diffusion.DiffusionInterface(T_1, T_2, D_1, D_2, dim=3, time=True)[source]

Bases: PDE

Matches the boundary conditions at an interface

Parameters

• T_1 (str) – Dependent variables to match the boundary conditions at the interface.

• T_2 (str) – Dependent variables to match the boundary conditions at the interface.

• D_1 (float) – Diffusivity at the interface.

• D_2 (float) – Diffusivity at the interface.

• dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

Example

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>>> diff = DiffusionInterface('theta_s', 'theta_f', 0.1, 0.05, dim=2)
>>> diff.pprint()
diffusion_interface_dirichlet_theta_s_theta_f: -theta_f + theta_s
diffusion_interface_neumann_theta_s_theta_f: -0.05*normal_x*theta_f__x
+ 0.1*normal_x*theta_s__x - 0.05*normal_y*theta_f__y
+ 0.1*normal_y*theta_s__y
        

Maxwell’s equation

class modulus.sym.eq.pdes.electromagnetic.MaxwellFreqReal(ux='ux', uy='uy', uz='uz', k=1.0, mixed_form=False)[source]

Bases: PDE

Frequency domain Maxwell’s equation

Parameters

• ux (str) – Ex

• uy (str) – Ey

• uz (str) – Ez

• k (float, Sympy Symbol/Expr, str) – Wave number. If k is a str then it is converted to Sympy Function of form ‘k(x,y,z,t)’. If ‘k’ is a Sympy Symbol or Expression then this is substituted into the equation.

• mixed_form (bool) – If True, use the mixed formulation of the diffusion equations.

class modulus.sym.eq.pdes.electromagnetic.PEC(ux='ux', uy='uy', uz='uz', dim=3)[source]

Bases: PDE

Perfect Electric Conduct BC for

Parameters

• ux (str) – Ex

• uy (str) – Ey

• uz (str) – Ez

• dim (int) – Dimension of the equations (2, or 3). Default is 3.

class modulus.sym.eq.pdes.electromagnetic.SommerfeldBC(ux='ux', uy='uy', uz='uz')[source]

Bases: PDE

Frequency domain ABC, Sommerfeld radiation condition Only for real part Equation: ‘n x _curl(E) = 0’

Parameters

• ux (str) – Ex

• uy (str) – Ey

• uz (str) – Ez

Equations related to linear elasticity

class modulus.sym.eq.pdes.linear_elasticity.LinearElasticity(E=None, nu=None, lambda_=None, mu=None, rho=1, dim=3, time=False)[source]

Bases: PDE

Linear elasticity equations. Use either (E, nu) or (lambda_, mu) to define the material properties.

Parameters

• E (float, Sympy Symbol/Expr, str) – The Young’s modulus

• nu (float, Sympy Symbol/Expr, str) – The Poisson’s ratio

• lambda (float, Sympy Symbol/Expr, str) – Lamé’s first parameter

• mu (float, Sympy Symbol/Expr, str) – Lamé’s second parameter (shear modulus)

• rho (float, Sympy Symbol/Expr, str) – Mass density.

• dim (int) – Dimension of the linear elasticity (2 or 3). Default is 3.

Example

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>>> elasticity_equations = LinearElasticity(E=10, nu=0.3, dim=2)
>>> elasticity_equations.pprint()
navier_x: -13.4615384615385*u__x__x - 3.84615384615385*u__y__y - 9.61538461538461*v__x__y
navier_y: -3.84615384615385*v__x__x - 13.4615384615385*v__y__y - 9.61538461538461*u__x__y
stress_disp_xx: -sigma_xx + 13.4615384615385*u__x + 5.76923076923077*v__y
stress_disp_yy: -sigma_yy + 5.76923076923077*u__x + 13.4615384615385*v__y
stress_disp_xy: -sigma_xy + 3.84615384615385*u__y + 3.84615384615385*v__x
equilibrium_x: -sigma_xx__x - sigma_xy__y
equilibrium_y: -sigma_xy__x - sigma_yy__y
traction_x: normal_x*sigma_xx + normal_y*sigma_xy
traction_y: normal_x*sigma_xy + normal_y*sigma_yy
        

class modulus.sym.eq.pdes.linear_elasticity.LinearElasticityPlaneStress(E=None, nu=None, lambda_=None, mu=None, rho=1, time=False)[source]

Bases: PDE

Linear elasticity plane stress equations. Use either (E, nu) or (lambda_, mu) to define the material properties.

Parameters

• E (float, Sympy Symbol/Expr, str) – The Young’s modulus

• nu (float, Sympy Symbol/Expr, str) – The Poisson’s ratio

• lambda (float, Sympy Symbol/Expr, str) – Lamé’s first parameter.

• mu (float, Sympy Symbol/Expr, str) – Lamé’s second parameter (shear modulus)

• rho (float, Sympy Symbol/Expr, str) – Mass density.

Example

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>>> plane_stress_equations = LinearElasticityPlaneStress(E=10, nu=0.3)
>>> plane_stress_equations.pprint()
stress_disp_xx: -sigma_xx + 10.989010989011*u__x + 3.2967032967033*v__y
stress_disp_yy: -sigma_yy + 3.2967032967033*u__x + 10.989010989011*v__y
stress_disp_xy: -sigma_xy + 3.84615384615385*u__y + 3.84615384615385*v__x
equilibrium_x: -sigma_xx__x - sigma_xy__y
equilibrium_y: -sigma_xy__x - sigma_yy__y
traction_x: normal_x*sigma_xx + normal_y*sigma_xy
traction_y: normal_x*sigma_xy + normal_y*sigma_yy
        

Equations related to Navier Stokes Equations

class modulus.sym.eq.pdes.navier_stokes.CompressibleIntegralContinuity(rho=1, vec=['u', 'v', 'w'])[source]

Bases: PDE

Compressible Integral Continuity

Parameters

• rho (float, Sympy Symbol/Expr, str) – The density of the fluid. If rho is a str then it is converted to Sympy Function of form ‘rho(x,y,z,t)’. If ‘rho’ is a Sympy Symbol or Expression then this is substituted into the equation to allow for compressibility. Default is 1.

• dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

class modulus.sym.eq.pdes.navier_stokes.Curl(vector, curl_name=['u', 'v', 'w'])[source]

Bases: PDE

del cross vector operator

Parameters

• vector (tuple of 3 Sympy Exprs, floats, ints or strings) – This will be the vector to take the curl of.

• curl_name (tuple of 3 strings) – These will be the output names of the curl operations.

Examples

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>>> c = Curl((0,0,'phi'), ('u','v','w'))
>>> c.pprint()
u: phi__y
v: -phi__x
w: 0
        

class modulus.sym.eq.pdes.navier_stokes.FluxContinuity(T='T', D='D', rho=1, vec=['u', 'v', 'w'])[source]

Bases: PDE

Flux Continuity for arbitrary variable. Includes advective and diffusive flux

Parameters

• T (str) – The dependent variable.

• rho (float, Sympy Symbol/Expr, str) – The density of the fluid. If rho is a str then it is converted to Sympy Function of form ‘rho(x,y,z,t)’. If ‘rho’ is a Sympy Symbol or Expression then this is substituted into the equation to allow for compressibility. Default is 1.

• dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

class modulus.sym.eq.pdes.navier_stokes.GradNormal(T, dim=3, time=True)[source]

Bases: PDE

Implementation of the gradient boundary condition

Parameters

• T (str) – The dependent variable.

• dim (int) – Dimension of the equations (1, 2, or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

Examples

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>>> gn = ns = GradNormal(T='T')
>>> gn.pprint()
normal_gradient_T: normal_x*T__x + normal_y*T__y + normal_z*T__z
        

class modulus.sym.eq.pdes.navier_stokes.NavierStokes(nu, rho=1, dim=3, time=True, mixed_form=False)[source]

Bases: PDE

Compressible Navier Stokes equations

Parameters

• nu (float, Sympy Symbol/Expr, str) – The kinematic viscosity. If nu is a str then it is converted to Sympy Function of form nu(x,y,z,t). If nu is a Sympy Symbol or Expression then this is substituted into the equation. This allows for variable viscosity.

• rho (float, Sympy Symbol/Expr, str) – The density of the fluid. If rho is a str then it is converted to Sympy Function of form ‘rho(x,y,z,t)’. If ‘rho’ is a Sympy Symbol or Expression then this is substituted into the equation to allow for compressible Navier Stokes. Default is 1.

• dim (int) – Dimension of the Navier Stokes (2 or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

• mixed_form (bool) – If True, use the mixed formulation of the Navier-Stokes equations.

Examples

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>>> ns = NavierStokes(nu=0.01, rho=1, dim=2)
>>> ns.pprint()
continuity: u__x + v__y
momentum_x: u*u__x + v*u__y + p__x + u__t - 0.01*u__x__x - 0.01*u__y__y
momentum_y: u*v__x + v*v__y + p__y + v__t - 0.01*v__x__x - 0.01*v__y__y
>>> ns = NavierStokes(nu='nu', rho=1, dim=2, time=False)
>>> ns.pprint()
continuity: u__x + v__y
momentum_x: -nu*u__x__x - nu*u__y__y + u*u__x + v*u__y - nu__x*u__x - nu__y*u__y + p__x
momentum_y: -nu*v__x__x - nu*v__y__y + u*v__x + v*v__y - nu__x*v__x - nu__y*v__y + p__y
        

Screened Poisson Distance Equation taken from, https://www.researchgate.net/publication/266149392_Dynamic_Distance-Based_Shape_Features_for_Gait_Recognition, Equation 6 in paper.

class modulus.sym.eq.pdes.signed_distance_function.ScreenedPoissonDistance(distance='normal_distance', tau=0.1, dim=3)[source]

Bases: PDE

Screened Poisson Distance

Parameters

• distance (str) – A user-defined variable for distance. Default is “normal_distance”.

• tau (float) – A small, positive parameter. Default is 0.1.

• dim (int) – Dimension of the Screened Poisson Distance (1, 2, or 3). Default is 3.

Example

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>>> s = ScreenedPoissonDistance(tau=0.1, dim=2)
>>> s.pprint()
screened_poisson_normal_distance: -normal_distance__x**2
+ 0.316227766016838*normal_distance__x__x - normal_distance__y**2
+ 0.316227766016838*normal_distance__y__y + 1
        

Zero Equation Turbulence model References: https://www.eureka.im/954.html https://knowledge.autodesk.com/support/cfd/learn-explore/caas/CloudHelp/cloudhelp/2019/ENU/SimCFD-Learning/files/GUID-BBA4E008-8346-465B-9FD3-D193CF108AF0-htm.html

class modulus.sym.eq.pdes.turbulence_zero_eq.ZeroEquation(nu, max_distance, rho=1, dim=3, time=True)[source]

Bases: PDE

Zero Equation Turbulence model

Parameters

• nu (float) – The kinematic viscosity of the fluid.

• max_distance (float) – The maximum wall distance in the flow field.

• rho (float, Sympy Symbol/Expr, str) – The density. If rho is a str then it is converted to Sympy Function of form ‘rho(x,y,z,t)’. If ‘rho’ is a Sympy Symbol or Expression then this is substituted into the equation. Default is 1.

• dim (int) – Dimension of the Zero Equation Turbulence model (2 or 3). Default is 3.

• time (bool) – If time-dependent equations or not. Default is True.

Example

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>>> zeroEq = ZeroEquation(nu=0.1, max_distance=2.0, dim=2)
>>> kEp.pprint()
nu: sqrt((u__y + v__x)**2 + 2*u__x**2 + 2*v__y**2)
*Min(0.18, 0.419*normal_distance)**2 + 0.1
        

Wave equation Reference: https://en.wikipedia.org/wiki/Wave_equation

class modulus.sym.eq.pdes.wave_equation.HelmholtzEquation(u, k, dim=3, mixed_form=False)[source]

Bases: PDE

class modulus.sym.eq.pdes.wave_equation.WaveEquation(u='u', c='c', dim=3, time=True, mixed_form=False)[source]

Bases: PDE

Wave equation

Parameters

• u (str) – The dependent variable.

• c (float, Sympy Symbol/Expr, str) – Wave speed coefficient. If c is a str then it is converted to Sympy Function of form ‘c(x,y,z,t)’. If ‘c’ is a Sympy Symbol or Expression then this is substituted into the equation.

• dim (int) – Dimension of the wave equation (1, 2, or 3). Default is 2.

• time (bool) – If time-dependent equations or not. Default is True.

• mixed_form (bool) – If True, use the mixed formulation of the wave equation.

Examples

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>>> we = WaveEquation(c=0.8, dim=3)
>>> we.pprint()
wave_equation: u__t__t - 0.64*u__x__x - 0.64*u__y__y - 0.64*u__z__z
>>> we = WaveEquation(c='c', dim=2, time=False)
>>> we.pprint()
wave_equation: -c**2*u__x__x - c**2*u__y__y - 2*c*c__x*u__x - 2*c*c__y*u__y
        

class modulus.sym.eq.derivatives.Derivative(bwd_derivative_dict: Dict[Key, List[Key]])[source]

Bases: Module

Module to compute derivatives using backward automatic differentiation

forward(input_var: Dict[str, Tensor]) → Dict[str, Tensor][source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

class modulus.sym.eq.derivatives.MeshlessFiniteDerivative(model: Module, derivatives: List[Key], dx: Union[float, Callable], order: int = 2, max_batch_size: Optional[int] = None, double_cast: bool = True, input_keys: Optional[List[Key]] = None)[source]

Bases: Module

Module to compute derivatives using meshless finite difference

Parameters

• model (torch.nn.Module) – Forward torch module for calculating stencil values

• derivatives (List[Key]) – List of derivative keys to calculate

• dx (Union[float, Callable]) – Spatial discretization of all axis, can be function with parameter count which is the number of forward passes for dynamically adjusting dx

• order (int, optional) – Order of derivative, by default 2

• max_batch_size (Union[int, None], optional) – Max batch size of stencil calucations, by default uses batch size of inputs

• double_cast (bool, optional) – Cast fields to double precision to calculate derivatives, by default True

• jit (bool, optional) – Use torch script for finite deriv calcs, by default True

forward(inputs: Dict[str, Tensor]) → Dict[str, Tensor][source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

classmethod make_node(node_model: Union[Node, Module], derivatives: List[Key], dx: Union[float, Callable], order: int = 2, max_batch_size: Optional[int] = None, name: Optional[str] = None, double_cast: bool = True, input_keys: Optional[Union[List[Key], List[str]]] = None)[source]

Makes a meshless finite derivative node.

Parameters

• node_model (Union[Node, torch.nn.Module]) – Node or torch.nn.Module for computing FD stencil values. Part of the inputs to this model should consist of the independent variables and output the functional value

• derivatives (List[Key]) – List of derivatives to be computed

• dx (Union[float, Callable]) – Spatial discretization for finite diff calcs, can be function

• order (int, optional) – Order of accuracy of finite diff calcs, by default 2

• max_batch_size (Union[int, None], optional) – Maximum batch size to used with the stenicl foward passes, by default None

• name (str, optional) – Name of node, by default None

• double_cast (bool, optional) – Cast tensors to double precision for derivatives, by default True

• input_keys (Union[List[Key], List[str], None], optional) – List of input keys to be used for input of forward model. Should be used if node_model is not a Node, by default None