BDF.cpp

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#include "BDF.hpp"
 
#include <polyfem/utils/Logger.hpp>
 
namespace polyfem::time_integrator
{
    BDF::BDF(const int order)
    {
        if (order < 1 || order > 6)
            log_and_throw_error("BDF order must be 1 ≤ n ≤ 6");
        max_steps_ = order;
    }
 
    void BDF::set_parameters(const json &params)
    {
        max_steps_ = params.at("steps");
        if (max_steps_ < 1 || max_steps_ > 6)
            log_and_throw_error("BDF steps must be 1 ≤ n ≤ 6");
    }
 
    const std::vector<double> &BDF::alphas(const int i)
    {
        static const std::array<std::vector<double>, 6> _alphas = {{
            {1},
            {4.0 / 3.0, -1.0 / 3.0},
            {18.0 / 11.0, -9.0 / 11.0, 2.0 / 11.0},
            {48.0 / 25.0, -36.0 / 25.0, 16.0 / 25.0, -3.0 / 25.0},
            {300.0 / 137.0, -300.0 / 137.0, 200.0 / 137.0, -75.0 / 137.0, 12.0 / 137.0},
            {360.0 / 147.0, -450.0 / 147.0, 400.0 / 147.0, -225.0 / 147.0, 72.0 / 147.0, -10.0 / 147.0},
        }};
        assert(i >= 0 && i < _alphas.size());
        return _alphas[i];
    }
 
    double BDF::betas(const int i)
    {
        static const std::array<double, 6> _betas = {{
            1.0,
            2.0 / 3.0,
            6.0 / 11.0,
            12.0 / 25.0,
            60.0 / 137.0,
            60.0 / 147.0,
        }};
        assert(i >= 0 && i < _betas.size());
        return _betas[i];
    }
 
    Eigen::VectorXd BDF::weighted_sum_x_prevs() const
    {
        const std::vector<double> &alpha = alphas(steps() - 1);
 
        Eigen::VectorXd sum = Eigen::VectorXd::Zero(x_prev().size());
        for (int i = 0; i < steps(); i++)
        {
            sum += alpha[i] * x_prevs_[i];
        }
 
        return sum;
    }
 
    Eigen::VectorXd BDF::weighted_sum_v_prevs() const
    {
        const std::vector<double> &alpha = alphas(steps() - 1);
 
        Eigen::VectorXd sum = Eigen::VectorXd::Zero(v_prev().size());
        for (int i = 0; i < steps(); i++)
        {
            sum += alpha[i] * v_prevs_[i];
        }
 
        return sum;
    }
 
    void BDF::update_quantities(const Eigen::VectorXd &x)
    {
        const Eigen::VectorXd v = compute_velocity(x);
        const Eigen::VectorXd a = compute_acceleration(v);
 
        x_prevs_.push_front(x);
        v_prevs_.push_front(v);
        a_prevs_.push_front(a);
 
        if (steps() > max_steps())
        {
            x_prevs_.pop_back();
            v_prevs_.pop_back();
            a_prevs_.pop_back();
        }
        assert(x_prevs_.size() <= max_steps());
        assert(x_prevs_.size() == v_prevs_.size());
        assert(x_prevs_.size() == a_prevs_.size());
    }
 
    Eigen::VectorXd BDF::x_tilde() const
    {
        return weighted_sum_x_prevs() + betas(steps() - 1) * dt() * weighted_sum_v_prevs();
    }
 
    Eigen::VectorXd BDF::compute_velocity(const Eigen::VectorXd &x) const
    {
        return (x - weighted_sum_x_prevs()) / beta_dt();
    }
 
    Eigen::VectorXd BDF::compute_acceleration(const Eigen::VectorXd &v) const
    {
        return (v - weighted_sum_v_prevs()) / beta_dt();
    }
 
    double BDF::acceleration_scaling() const
    {
        const double beta = betas(steps() - 1);
        return beta * beta * dt() * dt();
    }
 
    double BDF::dv_dx(const unsigned i) const
    {
        if (i == 0)
            return 1 / beta_dt();
        if (i >= steps())
            return 0;
        return -alphas(steps() - 1)[i] / beta_dt();
    }
 
    double BDF::beta_dt() const
    {
        return betas(steps() - 1) * dt();
    }
} // namespace polyfem::time_integrator