| Abstract: |
| We present a numerical method for solving high-dimensional Mean Field Games systems. The approach combines semi-Lagrangian time discretizations with Tensor-Train decompositions to mitigate the curse of dimensionality. A smoothed policy iteration algorithm is approximated using first- and second-order schemes in time. The resulting method reduces storage and computational costs from exponential to polynomial growth in the dimension. Numerical experiments illustrate the accuracy, robustness, and scalability of the proposed approach. |
|