A new entropy-variable-based discretization method for minimum entropy moment approximations of linear kinetic equations

In this contribution we derive and analyze a new numerical method for kinetic equations based on variable transformation of the moment approximation. Classical minimum-entropy closures are class reduced models that conserve many fundamental physical properties solutions. However, their practical use is limited by high computational cost, as an optimization problem has to be solved every cell in space-time grid. addition, implementation solvers these hampered fact problems only well-defined if vectors stay within realizable set. For same reason, further reducing by, e.g. , reduced-basis methods not simple task. Our overcomes disadvantages classical approaches. The performed semi-discretized level which makes them applicable wide range schemes replaces nonlinear inversion positive-definite Hessian matrix. As result, scheme gets rid realizability-related problems. Moreover, discrete entropy law can enforced modifying time stepping scheme. experiments demonstrate our often several times faster than standard optimization-based