view +time/Rungekutta.m @ 1037:2d7ba44340d0 feature/burgers1d

Pass scheme specific parameters as cell array. This will enabale constructDiffOps to be more general. In addition, allow for schemes returning function handles as diffOps, which is currently how non-linear schemes such as Burgers1d are implemented.
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Fri, 18 Jan 2019 09:02:02 +0100
parents 4e5e53d6336c
children
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classdef Rungekutta < time.Timestepper
    properties
        F       % RHS of the ODE
        k       % Time step
        t       % Time point
        v       % Solution vector
        n       % Time level
        scheme  % The scheme used for the time stepping, e.g rk4, rk6 etc.
    end


    methods
        % Timesteps v_t = F(v,t), using RK with specfied order from t = t0 with
        % timestep k and initial conditions v = v0
        function obj = Rungekutta(F, k, t0, v0, order)
            default_arg('order',4);
            obj.F = F;
            obj.k = k;
            obj.t = t0;
            obj.v = v0;
            obj.n = 0;
            % TBD: Order 4 is also implemented in the butcher tableau, but the rungekutta_4.m implementation
            % might be slightly more efficient. Need to do some profiling before deciding whether or not to keep it.
            if (order == 4)
                obj.scheme = @time.rk.rungekutta_4;
            else
                % Extract the coefficients for the specified order
                % used for the RK updates from the Butcher tableua.
                [s,a,b,c] = time.rk.butcherTableau(order);
                coeffs = struct('s',s,'a',a,'b',b,'c',c);
                obj.scheme = @(v,t,dt,F) time.rk.rungekutta(v, t , dt, F, coeffs);
            end
        end

        function [v,t] = getV(obj)
            v = obj.v;
            t = obj.t;
        end

        function obj = step(obj)
            obj.v = obj.scheme(obj.v, obj.t, obj.k, obj.F);
            obj.t = obj.t + obj.k;
            obj.n = obj.n + 1;
        end
    end
end