# Ordinary Differential Equations Integration

## Overview

The ode package provides classes to solve Ordinary Differential Equations problems.

This package solves Initial Value Problems of the form `\(y'=f(t,y)\)` with `\(t_0\)` and `\(y(t_0)=y_0\)` known. The `\(f\)` function is the Ordinary Differential Equation to be solved. The provided integrators compute an estimate of `\(y(t)\)` from `\(t=t_0\)` to `\(t=t_1\)`.

All integrators provide dense output. This means that besides computing the state vector at discrete times, they also provide a cheap mean to get both the state and its derivative between the time steps. They do so through classes extending the ODEStateInterpolator abstract class, which are made available to the user at the end of each step.

All integrators handle multiple discrete events detection based on switching functions. This means that the integrator can be driven by user specified discrete events (occurring when the sign of user-supplied *switching function* changes). The steps are shortened as needed to ensure the events occur at step boundaries (even if the integrator is a fixed-step integrator). When the events are triggered, integration can be stopped (this is called a G-stop facility), the state vector can be changed, or integration can simply go on. The latter case is useful to handle discontinuities in the differential equations gracefully and get accurate dense output even close to the discontinuity.

All integrators support setting a maximal number of evaluations of differential equations function. If this number is exceeded, an exception will be thrown during integration. This can be used to prevent infinite loops if for example error control or discrete events create a really large number of extremely small steps. By default, the maximal number of evaluation is set to `Integer.MAX_VALUE` (i.e. `\(2^{31}-1\)` or 2147483647). It is recommended to set this maximal number to a value suited to the ODE problem, integration range, and step size or error control settings.

All integrators support expanding the primary ODE with one or more secondary ODE to manage additional states that will be integrated together with the primary state. This can be used for example to compute partial derivatives of the primary state with respect to either the initial state or some parameters (see below for an example).

Two parallel APIs are available. The first is devoted to solve ode for which the integration free variable t and the state y(t) are primitive double and primitive double array respectively. The second API is devoted to solve ode for which the integration free variable t and the state y(t) are RealFieldElement and RealFieldElement array respectively. This allow for example users to integrate ode where the computation values are for example DerivativeStructure elements, hence automatically computing partial derivatives with respect to some equations parameters without a need to set up the variational equations manually or if derivatives with orders higher than 1 is needed, which cannot be handled by variational equations. Another example is to use Dfp elements in order to solve ode with extended precision.

## Basic Use

The user should describe his problem in his own classes which should implement the OrdinaryDifferentialEquation interface (or FieldOrdinaryDifferentialEquation interface). The following example shows how to implement a simple two-dimensional problem using double primitives: `\[ \begin{align*} y'_0 (t) &= \omega \times (c_1 - y_1 (t))\\ y'_1 (t) &= \omega \times (y_0 (t) - c_0) \end{align*} \]`

with some initial state `\(y(t_0) = (y_0(t_0), y_1(t_0))\)`. In fact, the exact solution of this problem is that `\(y(t)\)` moves along a circle centered at `\(c = (c_0, c_1)\)` with constant angular rate `\(\omega\)`.

private static class Circle1 implements OrdinaryDifferentialEquation { private double[] c; private double omega; public Circle1(double[] c, double omega) { this.c = c; this.omega = omega; } public int getDimension() { return 2; } public double[] computeDerivatives(double t, double[] y) { return new double[] { omega * (c[1] - y[1]), omega * (y[0] - c[0]) }; } }

The OrdinaryDifferentialEquation interface also defines an `init` method that can be used is some initialization must be performed at the start of the integration. There is a default implementation that does nothing, so the method must be implemented only if really needed.

The user must also set up an initial state as an ODEState instance. Then he should pass both the equations and the initial state to the integrator he prefers among all the classes that implement the ODEIntegrator interface (or the FieldODEIntegrator interface). Computing the state `\(y(16.0)\)` starting from `\(y(0.0) = (0.0, 1.0)\)` and integrating the ODE is done as follows (using Dormand-Prince 8(5,3) integrator as an example):

ODEIntegrator dp853 = new DormandPrince853Integrator(1.0e-8, 100.0, 1.0e-10, 1.0e-10); OrdinaryDifferentialEquation ode = new Circle1(new double[] { 1.0, 1.0 }, 0.1); ODEState initialState = new ODEState(0.0, new double[] { 0.0, 1.0 }); ODEStateAndDerivative finalState = dp853.integrate(ode, initialState, 16.0); double t = finalState.getTime(); double[] y = finalState.getPrimaryState(); System.out.format(Locale.US, "final state at %4.1f: %6.3f %6.3f%n", t, y[0], y[1]);

## Dense Output

The solution of the integration problem is provided by two means. The first one, shown in the previous example, is aimed towards simple use: the state vector at the end of the integration process is returned by the `ODEIntegrator.integrate` method, in an ODEStateAndDerivative instance. The second one should be used when more in-depth information is needed throughout the integration process and not only at the end. The user can register an object implementing the ODEStepHandler interface or a StepNormalizer object wrapping a user-specified object implementing the ODEFixedStepHandler interface into the integrator before calling the `ODEIntegrator.integrate` method. The user object will be called appropriately during the integration process, allowing the user to monitor intermediate results. The default step handler does nothing. Considering again the previous example, we want to print the trajectory of the point to check it really is a circle arc. We simply add the following before the call to `dp853.integrate`:

ODEStepHandler stepHandler = new ODEStepHandler() { public void handleStep(ODEStateInterpolator interpolator, boolean isLast) { double stepStart = interpolator.getPreviousState().getTime(); double stepEnd = interpolator.getPreviousState().getTime(); for (int i = 0; i < 20; ++i) { // we want to print 20 points for each step double t = ((20 - i) * stepStart + i * stepEnd) / 20; double[] y = interpolator.getInterpolatedState(t).getPrimaryState(); System.out.println(t + " " + y[0] + " " + y[1]); } } }; dp853.addStepHandler(stepHandler);

The ODEStepHandler interface also defines an `init` method that can be used is some initialization must be performed at the start of the integration. There is a default implementation that does nothing, so the method must be implemented only if really needed.

DenseOutputModel is a special-purpose step handler that is able to store all steps and to provide transparent access to any intermediate result once the integration is over. An important feature of this class is that it implements the `Serializable` interface. This means that a complete continuous model of the integrated function throughout the integration range can be serialized and reused later (if stored into a persistent medium like a file system or a database) or elsewhere (if sent to another application). Only the result of the integration is stored, there is no reference to the integrated problem by itself.

Another predefined implementations of the ODEStepHandler interface, StepNormalizer) is devoted to normalize steps to a fixed size even if the integrator is a variable step integrator. This allows for example to print a regular ephemeris for a trajectory.

Specific implementations can be developed by users for specific needs. As an example, if an application is to be completely driven by the integration process, then most of the application code will be run inside a step handler specific to this application.

Some integrators (the simple ones) use fixed steps that are set at creation time. The more efficient integrators use variable steps that are handled internally in order to control the local integration error of the main state with respect to a specified accuracy (these integrators extend the AdaptiveStepsizeIntegrator abstract class). The secondary equations are explicitly ignored for step size control, in order to get reproducible results regardless of the secondary equations being integrated or not. The step handler which is called after each successful step shows up the variable stepsize. The StepNormalizer class can be used to convert the variable stepsize into a fixed stepsize that can be handled by classes implementing the FixedStepHandler interface. Adaptive stepsize integrators can automatically compute the initial stepsize by themselves, however the user can specify it if he prefers to retain full control over the integration or if the automatic guess is wrong.

## Discrete Events Handling

ODE problems are continuous ones. However, sometimes discrete events may be taken into account. The most frequent case is the stop condition of the integrator is not defined by the time t but by a target condition on state `\(y\)` (say `\(y[0] = 1.0\)` for example).

Discrete events detection is based on switching functions. The user provides a simple g(state) switching function depending on the current state. The integrator will monitor the value of the function throughout integration range and will trigger the event when its sign changes. The magnitude of the value is almost irrelevant. For the sake of root finding, it should however be continuous (but not necessarily smooth) at least in the roots vicinity. The steps are shortened as needed to ensure the events occur at step boundaries (even if the integrator is a fixed-step integrator).

When an event is triggered (i.e. when the sign of the `g` switching function changes), the event state and an indicator whether the switching function was increasing or decreasing at event occurrence are provided to the user. Several different options are available to deal with the event occurrence.

The first case, G-stop, is the most common one. A typical use case is when an ODE must be integrated until some target state is reached, with a known value of the state but an unknown occurrence time. As an example, if we want to monitor a chemical reaction until some predefined concentration for the first substance, we can use the following switching function setting:

public double g(ODEStateAndDerivative state) { return state.getPrimaryState()[0] - targetConcentration; } public Action eventOccurred(ODEStateAndDerivative state, boolean increasing) { return Action.STOP; }

The second case correspond to discontinuous dynamical models, for which state vector or derivatives must be changed instantaneously when the event occurs. A typical case would be the motion of a spacecraft when thrusters are fired for orbital maneuvers. The acceleration is smooth as long as no maneuvers are performed, depending only on gravity, drag, third body attraction, radiation pressure. Firing a thruster introduces a discontinuity on the acceleration that must be handled appropriately by the integrator. In such a case, we would use a switching function setting similar to this:

public double g(ODEStateAndDerivative state) { final double t = state.getTime(); return (t - tManeuverStart) * (t - tManeuverStop); } public Action eventOccurred(ODEStateAndDerivative state, boolean increasing) { return Action.RESET_DERIVATIVES; }

The third case is useful mainly for monitoring purposes, when we simply want to log the event but otherwise do not interfere with the integration process. A simple example is:

public double g(ODEStateAndDerivative state) { final double[] y = state.getPrimaryState(); return y[0] - y[1]; } public Action eventOccurred(ODEStateAndDerivative state, boolean increasing) { logger.log("y0(t) and y1(t) curves cross at t = " + v state.getTime()); return Action.CONTINUE; }

The difference between the cases appears in the return value of the `eventOccurred` method. The integrator will call the user method when integration process reaches a point where the `g` switching function sign changes, then it will call the `eventOccurred` method and its behaviour will depend on the value returned by this call.

## Available Integrators

The tables below show the various integrators available for non-stiff problems. Note that the implementations of Adams-Bashforth and Adams-Moulton are adaptive stepsize, not fixed stepsize as is usual for these multi-step integrators. This is due to the fact the implementation relies on the Nordsieck vector representation of the state.

Fixed Step Integrators | |
---|---|

Name | Order |

Euler | 1 |

Midpoint | 2 |

Classical Runge-Kutta | 4 |

Gill | 4 |

3/8 | 4 |

Luther | 6 |

Adaptive Stepsize Integrators | ||
---|---|---|

Name | Integration Order | Error Estimation Order |

Higham and Hall | 5 | 4 |

Dormand-Prince 5(4) | 5 | 4 |

Dormand-Prince 8(5,3) | 8 | 5 and 3 |

Gragg-Bulirsch-Stoer | variable (up to 18 by default) | variable |

Adams-Bashforth | variable | variable |

Adams-Moulton | variable | variable |

## Secondary States

In some cases, the ordinary differential equations is split into a primary state and some additional, secondary states. This can be done by providing an ExpandableODE) instance to the integrator instead of a regular Ordinary Differential Equation and add SecondaryODE to this expandable equation.

The ODEState initial state and ODEStateAndDerivative final state contains data pertaining to both the primary state and all the secondary states.

The adaptive stepsize integrators rely only on the primary state for stepsize control by design. If some components should be used for step size control, then they must be part of the primary state and not added as secondary states.

## Derivatives

In some cases, the sensitivity `\(dy/dy_0\)` of the final state with respect to the initial state `\(y_0\)` (often called state transition matrix) or the sensitivity `\(dy/dp_k)\)` of the final state with respect to some parameters `\(p_k\)` of the ODE are needed, in addition to the final state `\(y(t)\)` itself.

This can be computed by using variational equations which automatically adds a set of secondary equations to the primary ODE and adds initial secondary states corresponding to the desired partial derivatives before the integration starts. Then the integration will propagate the compound state composed of both the primary state and its partial derivatives. At the end of the integration, the Jacobian matrices are extracted from the integrated secondary state. As the variational equations are considered to be secondary equations here, variable step integrators ignore them for step size control: they rely only on the main state. This feature is a design choice. The rationale is to get exactly the same steps, regardless of the Jacobians being computed or not, hence ensuring reproducible results in both cases.

If for example the primary state dimension is 6 and there are 3 parameters, the compound state will be a 60 elements array. The first 6 elements will be the primary state, the next 54 elements will be a secondary state containing first the `\(6 \times 6\)` Jacobian matrix of the final state with respect to the initial state, and 3 sets of 6 elements vectors for the Jacobian matrix of the final state with respect to the 3 parameters.

The VariationalEquation handles most of secondary equations and states management, as long as the local partial derivatives are provided to it. On both initialization of the matrices and extraction of the intermediate or final results from ODEStateAndDerivative instances, the state transition matrix is represented as a square `\(n \times n\)` array (for a dimension `\(n\)` primary state). The parameters are identified by a name (a simple user defined string) and the associated partial derivatives are dimension `\(n\)` arrays.

What remains of user responsibility is to provide the local Jacobians `\(df(t, y, p)/dy\)` and `\(df(t, y, p)/dp_k\)` corresponding the the main ODE `\(y'=f(t, y, p)\)`. There are two ways for user to provide theses local partial derivatives. The first way is to provide a full-fledged ODEJacobiansProvider which is an OrdinaryDifferentialEquation with additional capabilities to compute partial derivatives. The second way is to provide a simple OrdinaryDifferentialEquation and to configure a way to compute the required derivatives using finite differences.

Note that if the VariationalEquation instance is known by step handlers or event handlers, it is possible to retrieve the current values of the state transition matrix and the parameters derivatives during the integration process.

As the variational equation automatically inserts secondary differential equations in the expandable ODE, data for initial state must also be inserted before integration and matrices result must be extracted after integration. This implies a precise scheduling of the calls to the various methods of this class. The proper scheduling is the following one:

// set up equations ODEJacobiansProvider jode = new MyODE(...); ExpandableODE expandable = new Expandable(jode); VariationalEquation ve = new VariationalEquation(expandable, jode); // set up initial state ODEState initWithoutDerivatives = new ODEState(t0, y0); ve.setInitialMainStateJacobian(dYdY0); // only needed if the default identity matrix is not suitable ve.setInitialParameterJacobian(name, dYdP); // only needed if the default zero matrix is not suitable ODEState initWithDerivatives = ve.setUpInitialState(initWithoutDerivatives); // perform integration on the expanded equations with the expanded initial state ODEStateAndDerivative finalState = integrator.integrate(expandable, initWithDerivatives, finalT); // extract Jacobian matrices dYdY0 = ve.extractMainSetJacobian(finalState); dYdP = ve.extractParameterJacobian(finalState, name);

The most important part is to not forget to call `setUpInitialState` to add the secondary state with the initial matrices to the `ODEState` used in the `integrate` method. Forgetting to do this and passing only a `ODEState` without the secondary state set up will trigger an error as the state vector will not have the correct dimension.

The following example corresponds to a simple case where all derivatives can be computed analytically. The state is a 2D point traveling along a circle. There are three parameters: the two coordinates of the center and the angular velocity.

private static class Circle2 implements ODEJacobiansProvider { public static final String CX = "cx"; public static final String CY = "cy"; public static final String OMEGA = "omega"; private double cx; private double cy; private double omega; public Circle2(double cx, double cy, double omega) { this.cx = cx; this.cy = cy; this.omega = omega; } public int getDimension() { return 2; } public double[] computeDerivatives(double t, double[] y) { return new double[] { omega * (cy - y[1]), omega * (y[0] - cx) }; } public double[][] computeMainStateJacobian(double t, double[] y, double[] yDot) { return new double[][] { { 0, -omega }, { omega, 0 } }; } public List<String> getParametersNames() { return Arrays.asList(CX, CY, OMEGA); } public boolean isSupported(String name) { return CX.equals(name) || CY.equals(name) || OMEGA.equals(name); } public double[] computeParameterJacobian(double t, double[] y, double[] yDot, String paramName) throws MathIllegalArgumentException { if (CX.equals(paramName)) { return new double[] { 0, -omega }; } else if (CY.equals(paramName)) { return new double[] { omega, 0 }; } else if (OMEGA.equals(paramName)) { return new double[] { cy - y[1], y[0] - cx }; } else { throw new MathIllegalArgumentException(LocalizedODEFormats.UNKNOWN_PARAMETER, paramName); } } }

This ODE is integrated as follows:

ODEIntegrator integ = new DormandPrince54Integrator(1.0e-8, 100.0, 1.0e-10, 1.0e-10); Circle2 circle = new Circle2(1.0, 1.0, 0.1); ExpandableODE expandable = new ExpandableODE(circle); VariationalEquation ve = new VariationalEquation(expandable, circle); final ODEState initialState = ve.setUpInitialState(new ODEState(0, new double[] { 0.0, 1.0 })); final ODEStateAndDerivative finalState = integ.integrate(expandable, initialState, 18 * FastMath.PI); double[][] dYdY0 = ve.extractMainSetJacobian(finalState); System.out.format(Locale.US, "state transition matrixt at t = %6.3f%n %6.3f %6.3f%n %6.3f %6.3f%n", finalState.getTime(), dYdY0[0][0], dYdY0[0][1], dYdY0[1][0], dYdY0[1][1]); double[] dYdCX = ve.extractParameterJacobian(finalState, Circle.CX); System.out.format(Locale.US, "dY/dCx = %6.3f %6.3f%n", dYdCX[0], dYdCX[1]); double[] dYdCY = ve.extractParameterJacobian(finalState, Circle.CY); System.out.format(Locale.US, "dY/dCy = %6.3f %6.3f%n", dYdCY[0], dYdCY[1]); double[] dYdO = ve.extractParameterJacobian(finalState, Circle.OMEGA); System.out.format(Locale.US, "dY/dω = %6.3f %6.3f%n", dYdO[0], dYdO[1]);

We could have done a similar display throughout the integration by putting a similar code in a step handler.