Integration Schemes
All dynamic simulations assume to discretize the temporal evolution of the system through small time steps. This time step is usually noted dt. An integration scheme is the numerical method describing how to find the approximate solution for ordinary differential equations (ODE).
They are usually called ODESolver in SOFA.
Let’s write our ordinary differential equation of a function y as follows: .
ODESolver defines how to go from the current time step (t) to the next (t + dt), which will structure the linear system . The integration scheme therefore defines which forces impact the left hand side matrix and which forces contribute to the right hand side vector b:
 explicit contributions depending on the degrees of freedom (DOFs) at the current time step will contribute to the b vector
 while implicit contributions depending on the degrees of freedom (DOFs) at the next step (unknown) will contribute to .
Two categories
Two main categories of integration schemes exist: explicit and implicit schemes. A combination of explicit and implicit methods are also possible, it is called semiimplicit or semiexplicit schemes.
Explicit scheme
An explicit scheme means that the new time step (t + dt) is computed based on information of the previous time step (t):
For instance, in mechanics, internal or external forces would be computed on current known positions . The ordinary differential equation looks like:
Explicit schemes are usually known as being fast to solve (since the created linear system is lighter) but they require very small time steps, unless they may undergo stability issues. They are known to efficiently solve nonstiff problems.
Explicit ODESolvers in SOFA:
 EulerExplicitSolver
 CentralDifferenceSolver
 RungeKutta2Solver
Implicit scheme
An implicit scheme means that the new time step (t + dt) is computed based on information of this next time step (t + dt):
For instance, in mechanics, internal or external forces would be computed on unknow positions at the next time step . The ordinary differential equation looks like:
Implicit schemes are known as being slower to solve (the outcoming linear system is more complex) but they are way more stable than explicit schemes. Stiff differential equations require the use of implicit schemes.
Implicit ODESolvers in SOFA:
 EulerImplicitSolver
 NewmarkImplicitSolver
 VariationalSymplecticSolver
In the SOFA code
The integration scheme is described in the solve()
function of the ODESolver. This solve() function is called by the AnimationLoop (through a dedicated visitor) and builds the complete linear system .
Specification of the scheme
The construction of the linear system changes whether the integration scheme is explicit or implicit, which is specified by:

for explicit cases
mop>setImplicit(false);

for implicit cases
mop>setImplicit(true);
Build the linear matrix system
The left hand side matrix is built using the function:
matrix = MechanicalMatrix(r_M, r_B, r_K);
where r_M (mass coefficient), r_B (damping coefficient). and r_K (stiffness coefficient) are Rayleigh coefficients (see section below). Depending on the scheme (explicit or implicit, see previous paragraph) and on the type of LinearSolver used (if any), the abstract function MechanicalMatrix
will trigger different visitors, thus different functions to compute the system matrix . Discover the API used for the computation of in the ForceField and Mass doc pages.
The right hand side vector b is built through the function:
computeForce(b)
Again, Depending on the scheme (explicit or implicit, see previous paragraph), the abstract function computeForce
will trigger different visitors, thus different functions to accumulate the forces into the vector b. Discover the API used for the computation of b in the ForceField doc page.
State vectors in ODESolver
In order to build the linear matrix system, the ODESolver uses information contained in state vectors (like DOFs and their derivatives) within the scope of the ODESolver. The ODESolver does not access the state vectors directly. It accesses the state vectors remotely using visitors, which traverse the graph starting from the node which contains the solver. This keeps the implementation of the solver independent from the simulated objects and their types.
Each type of solver may use different auxiliary state vectors to implement their simulation method. State vectors (MultiVec) are allocated and processed in the scope of the solver in a threadsafe way using an instance of simulation::common::VectorOperations. For instance, a RungeKutta algorithms needs to save the result of previous time steps.
To create an auxiliary vector, this can be done as follows:
MultiVecCoord pos(&vop, core::VecCoordId::position() ); // standard position vector
MultiVecDeriv acc(&vop); // auxiliary vector
MultiVecCoord previousPos(&vop, previousPosID); // additional vector
Compute the solution
In most cases, the matrix system can then be sent to a LinearSolver in charge of finally solving the system defined according to the chosen scheme. Within the function ODESolver::solve(), the call to the LinearSolver will appear through the function call:
matrix.solve(x, b);
Some simple matrix cases provides a diagonal matrix . In this specific configuration, a solution can directly be found by dividing the right hand side vector b by the diagonal matrix . This is done using the function:
mop.accFromF(acc, f);
Rayleigh damping
The Rayleigh damping is a numerical damping. This damping has therefore no physical meaning and must not be mixed up with physical damping (like DiagonalVelocityDampingForceField in SOFA). The Rayleigh damping corresponds to a damping matrix that is proportional to the mass or/and stiffness matrices using ceofficients, respectively Rayleigh stiffness factor or Rayleigh mass factor . This numerical damping is usually used to stabilize or ease convergence of the simulation. However, it has to be used carefully.
When Rayleigh damping is used, the matrix equals where is the mass matrix, is the damping matrix and is the stiffness matrix. You can see the use of Rayleigh mass and stifness in the solve() function of the EulerImplicit class (see EulerImplicitSolver.cpp).
Last modified: 12 July 2019