A tensor decomposition algorithm for large ODEs with conservation laws
For researchers solving high-dimensional evolutionary equations, this method offers a way to handle large systems with conservation laws more efficiently than existing approaches.
The paper proposes a tensor train decomposition algorithm for solving high-dimensional ODEs and discretized PDEs that preserves conservation laws exactly. The method is shown to be faster than traditional time stepping and stochastic simulation algorithms for transport and chemical master equations, with invariants preserved to machine precision.
We propose an algorithm for solution of high-dimensional evolutionary equations (ODEs and discretized time-dependent PDEs) in the Tensor Train (TT) decomposition, assuming that the solution and the right-hand side of the ODE admit such a decomposition with a low storage. A linear ODE, discretized via one-step or Chebyshev differentiation schemes, turns into a large linear system. The tensor decomposition allows to solve this system for several time points simultaneously using an extension of the Alternating Least Squares algorithm. This method computes the TT approximation of the solution directly, without ever solving the original large problem, and encapsulates the Galerkin model reduction of the ODE. This allows an efficient estimation of the time discretization error, and hence provides a way to adapt the time steps. Besides, conservation laws can be preserved exactly in the reduced model by expanding the approximation subspace with the generating vectors of the linear invariants and correction of the euclidean norm. In numerical experiments with the transport and the chemical master equations, we demonstrate that the new method is faster than traditional time stepping and stochastic simulation algorithms, whereas the invariants are preserved up to the machine precision irrespectively of the TT approximation accuracy.