Struct diffsol::ode_solver::builder::OdeBuilder

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pub struct OdeBuilder { /* private fields */ }
Expand description

Builder for ODE problems. Use methods to set parameters and then call one of the build methods when done.

Implementations§

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impl OdeBuilder

Builder for ODE problems. Use methods to set parameters and then call one of the build methods when done.

§Example

use diffsol::{OdeBuilder, Bdf, OdeSolverState, OdeSolverMethod};
type M = nalgebra::DMatrix<f64>;

let problem = OdeBuilder::new()
  .rtol(1e-6)
  .p([0.1])
  .build_ode::<M, _, _, _>(
    // dy/dt = -ay
    |x, p, t, y| {
      y[0] = -p[0] * x[0];
    },
    // Jv = -av
    |x, p, t, v, y| {
      y[0] = -p[0] * v[0];
    },
    // y(0) = 1
   |p, t| {
      nalgebra::DVector::from_vec(vec![1.0])
   },
  ).unwrap();

let mut solver = Bdf::default();
let t = 0.4;
let mut state = OdeSolverState::new(&problem, &solver).unwrap();
solver.set_problem(state, &problem);
while solver.state().unwrap().t <= t {
    solver.step().unwrap();
}
let y = solver.interpolate(t);
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pub fn new() -> Self

Create a new builder with default parameters:

  • t0 = 0.0
  • h0 = 1.0
  • rtol = 1e-6
  • atol = [1e-6]
  • p = []
  • use_coloring = false
  • constant_mass = false
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pub fn t0(self, t0: f64) -> Self

Set the initial time.

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pub fn sensitivities(self, sensitivities: bool) -> Self

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pub fn sensitivities_error_control( self, sensitivities_error_control: bool ) -> Self

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pub fn h0(self, h0: f64) -> Self

Set the initial step size.

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pub fn rtol(self, rtol: f64) -> Self

Set the relative tolerance.

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pub fn atol<V, T>(self, atol: V) -> Self
where V: IntoIterator<Item = T>, f64: From<T>,

Set the absolute tolerance.

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pub fn p<V, T>(self, p: V) -> Self
where V: IntoIterator<Item = T>, f64: From<T>,

Set the parameters.

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pub fn use_coloring(self, use_coloring: bool) -> Self

Set whether to use coloring when computing the Jacobian. This can speed up the computation of the Jacobian for large sparse systems. However, it relys on the sparsity of the Jacobian being constant, and for certain systems it may detect the wrong sparsity pattern.

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pub fn build_ode_with_mass<M, F, G, H, I>( self, rhs: F, rhs_jac: G, mass: H, init: I ) -> Result<OdeSolverProblem<OdeSolverEquations<M, Closure<M, F, G>, ConstantClosure<M, I>, LinearClosure<M, H>>>>
where M: Matrix, F: Fn(&M::V, &M::V, M::T, &mut M::V), G: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), H: Fn(&M::V, &M::V, M::T, M::T, &mut M::V), I: Fn(&M::V, M::T) -> M::V,

Build an ODE problem with a mass matrix.

§Arguments
  • rhs: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the right-hand side of the ODE.
  • rhs_jac: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the Jacobian of the right-hand side with the vector v.
  • mass: Function of type Fn(v: &V, p: &V, t: S, beta: S, y: &mut V) that computes a gemv multiplication of the mass matrix with the vector v (i.e. y = M * v + beta * y).
  • init: Function of type Fn(p: &V, t: S) -> V that computes the initial state.
§Generic Arguments
  • M: Type that implements the Matrix trait. Often this must be provided explicitly (i.e. type M = DMatrix<f64>; builder.build_ode::<M, _, _, _>).
§Example
use diffsol::OdeBuilder;
use nalgebra::DVector;
type M = nalgebra::DMatrix<f64>;

// dy/dt = y
// 0 = z - y
// y(0) = 0.1
// z(0) = 0.1
let problem = OdeBuilder::new()
  .build_ode_with_mass::<M, _, _, _, _>(
      |x, _p, _t, y| {
          y[0] = x[0];
          y[1] = x[1] - x[0];
      },
      |x, _p, _t, v, y|  {
          y[0] = v[0];
          y[1] = v[1] - v[0];
      },
      |v, _p, _t, beta, y| {
          y[0] = v[0] + beta * y[0];
          y[1] = beta * y[1];
      },
      |p, _t| DVector::from_element(2, 0.1),
);
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pub fn build_ode_with_mass_and_sens<M, F, G, H, I, J, K, L>( self, rhs: F, rhs_jac: G, rhs_sens: J, mass: H, mass_sens: L, init: I, init_sens: K ) -> Result<OdeSolverProblem<OdeSolverEquations<M, ClosureWithSens<M, F, G, J>, ConstantClosureWithSens<M, I, K>, LinearClosureWithSens<M, H, L>>>>
where M: Matrix, F: Fn(&M::V, &M::V, M::T, &mut M::V), G: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), H: Fn(&M::V, &M::V, M::T, M::T, &mut M::V), I: Fn(&M::V, M::T) -> M::V, J: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), K: Fn(&M::V, M::T, &M::V, &mut M::V), L: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V),

Build an ODE problem with a mass matrix and sensitivities.

§Arguments
  • rhs: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the right-hand side of the ODE.
  • rhs_jac: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the Jacobian of the right-hand side with the vector v.
  • rhs_sens: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the partial derivative of the rhs wrt the parameters, with the vector v.
  • mass: Function of type Fn(v: &V, p: &V, t: S, beta: S, y: &mut V) that computes a gemv multiplication of the mass matrix with the vector v (i.e. y = M * v + beta * y).
  • mass_sens: Function of type Fn(v: &V, p: &V, t: S, y: &mut V) that computes the multiplication of the partial derivative of the mass matrix wrt the parameters, with the vector v.
  • init: Function of type Fn(p: &V, t: S) -> V that computes the initial state.
  • init_sens: Function of type Fn(p: &V, t: S, y: &mut V) that computes the multiplication of the partial derivative of the initial state wrt the parameters, with the vector v.
§Example
use diffsol::OdeBuilder;
use nalgebra::DVector;
type M = nalgebra::DMatrix<f64>;

// dy/dt = a y
// 0 = z - y
// y(0) = 0.1
// z(0) = 0.1
let problem = OdeBuilder::new()
  .build_ode_with_mass_and_sens::<M, _, _, _, _, _, _, _>(
      |x, p, _t, y| {
          y[0] = p[0] * x[0];
          y[1] = x[1] - x[0];
      },
      |x, p, _t, v, y|  {
          y[0] = p[0] * v[0];
          y[1] = v[1] - v[0];
      },
      |x, _p, _t, v, y| {
          y[0] = v[0] * x[0];
          y[1] = 0.0;
      },
      |x, _p, _t, beta, y| {
          y[0] = x[0] + beta * y[0];
          y[1] = beta * y[1];
      },
      |x, p, t, v, y| {
          y.fill(0.0);
      },
      |p, _t| DVector::from_element(2, 0.1),
      |p, t, v, y| {
          y.fill(0.0);
      }
);
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pub fn build_ode<M, F, G, I>( self, rhs: F, rhs_jac: G, init: I ) -> Result<OdeSolverProblem<OdeSolverEquations<M, Closure<M, F, G>, ConstantClosure<M, I>>>>
where M: Matrix, F: Fn(&M::V, &M::V, M::T, &mut M::V), G: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), I: Fn(&M::V, M::T) -> M::V,

Build an ODE problem with a mass matrix that is the identity matrix.

§Arguments
  • rhs: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the right-hand side of the ODE.
  • rhs_jac: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the Jacobian of the right-hand side with the vector v.
  • init: Function of type Fn(p: &V, t: S) -> V that computes the initial state.
§Generic Arguments
  • M: Type that implements the Matrix trait. Often this must be provided explicitly (i.e. type M = DMatrix<f64>; builder.build_ode::<M, _, _, _>).
§Example
use diffsol::OdeBuilder;
use nalgebra::DVector;
type M = nalgebra::DMatrix<f64>;


// dy/dt = y
// y(0) = 0.1
let problem = OdeBuilder::new()
   .build_ode::<M, _, _, _>(
       |x, _p, _t, y| y[0] = x[0],
       |x, _p, _t, v , y| y[0] = v[0],
       |p, _t| DVector::from_element(1, 0.1),
   );
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pub fn build_ode_with_sens<M, F, G, I, J, K>( self, rhs: F, rhs_jac: G, rhs_sens: J, init: I, init_sens: K ) -> Result<OdeSolverProblem<OdeSolverEquations<M, ClosureWithSens<M, F, G, J>, ConstantClosureWithSens<M, I, K>>>>
where M: Matrix, F: Fn(&M::V, &M::V, M::T, &mut M::V), G: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), I: Fn(&M::V, M::T) -> M::V, J: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), K: Fn(&M::V, M::T, &M::V, &mut M::V),

Build an ODE problem with a mass matrix that is the identity matrix and sensitivities.

§Arguments
  • rhs: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the right-hand side of the ODE.
  • rhs_jac: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the Jacobian of the right-hand side with the vector v.
  • rhs_sens: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the partial derivative of the rhs wrt the parameters, with the vector v.
  • init: Function of type Fn(p: &V, t: S) -> V that computes the initial state.
  • init_sens: Function of type Fn(p: &V, t: S, y: &mut V) that computes the multiplication of the partial derivative of the initial state wrt the parameters, with the vector v.
§Example
use diffsol::OdeBuilder;
use nalgebra::DVector;
type M = nalgebra::DMatrix<f64>;


// dy/dt = a y
// y(0) = 0.1
let problem = OdeBuilder::new()
   .build_ode_with_sens::<M, _, _, _, _, _>(
       |x, p, _t, y| y[0] = p[0] * x[0],
       |x, p, _t, v, y| y[0] = p[0] * v[0],
       |x, p, _t, v, y| y[0] = v[0] * x[0],
       |p, _t| DVector::from_element(1, 0.1),
       |p, t, v, y| y.fill(0.0),
   );
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pub fn build_ode_with_root<M, F, G, I, H>( self, rhs: F, rhs_jac: G, init: I, root: H, nroots: usize ) -> Result<OdeSolverProblem<OdeSolverEquations<M, Closure<M, F, G>, ConstantClosure<M, I>, UnitCallable<M>, ClosureNoJac<M, H>>>>
where M: Matrix, F: Fn(&M::V, &M::V, M::T, &mut M::V), G: Fn(&M::V, &M::V, M::T, &M::V, &mut M::V), H: Fn(&M::V, &M::V, M::T, &mut M::V), I: Fn(&M::V, M::T) -> M::V,

Build an ODE problem with an event.

§Arguments
  • rhs: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the right-hand side of the ODE.
  • rhs_jac: Function of type Fn(x: &V, p: &V, t: S, v: &V, y: &mut V) that computes the multiplication of the Jacobian of the right-hand side with the vector v.
  • init: Function of type Fn(p: &V, t: S) -> V that computes the initial state.
  • root: Function of type Fn(x: &V, p: &V, t: S, y: &mut V) that computes the root function.
  • nroots: Number of roots (i.e. number of elements in the y arg in root), an event is triggered when any of the roots changes sign.
§Generic Arguments
  • M: Type that implements the Matrix trait. Often this must be provided explicitly (i.e. type M = DMatrix<f64>; builder.build_ode::<M, _, _, _, _>).
§Example
use diffsol::OdeBuilder;
use nalgebra::DVector;
type M = nalgebra::DMatrix<f64>;


// dy/dt = y
// y(0) = 0.1
// event at y = 0.5
let problem = OdeBuilder::new()
   .build_ode_with_root::<M, _, _, _, _>(
       |x, _p, _t, y| y[0] = x[0],
       |x, _p, _t, v , y| y[0] = v[0],
       |p, _t| DVector::from_element(1, 0.1),
       |x, _p, _t, y| y[0] = x[0] - 0.5,
       1,
   );
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pub fn build_ode_dense<V, F, G, I>( self, rhs: F, rhs_jac: G, init: I ) -> Result<OdeSolverProblem<OdeSolverEquations<V::M, Closure<V::M, F, G>, ConstantClosure<V::M, I>>>>
where V: Vector + DefaultDenseMatrix, F: Fn(&V, &V, V::T, &mut V), G: Fn(&V, &V, V::T, &V, &mut V), I: Fn(&V, V::T) -> V,

Build an ODE problem using the default dense matrix (see Self::build_ode).

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pub fn build_diffsl( self, context: &DiffSlContext ) -> Result<OdeSolverProblem<DiffSl<'_>>>

Build an ODE problem using the DiffSL language (requires the diffsl feature). The source code is provided as a string, please see the DiffSL documentation for more information.

Trait Implementations§

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impl Default for OdeBuilder

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fn default() -> Self

Returns the “default value” for a type. Read more

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const ALIGN: usize = _

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type Init = T

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Initializes a with the given initializer. Read more
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