GPPI-AS offers a mesh-free, closed-form policy iteration solver for HJB and mean field game forward and inverse problems, with Schwarz acceleration cutting iterations roughly in half.
Lipschitz stability for determination of states and inverse source problem for the mean field game equations
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abstract
We consider solutions satisfying the zero Neumann boundary condition and a linearized mean field game equation in $\Omega \times (0,T)$ whose principal coefficients depend on the time and spatial variables with general Hamiltonian, where $\Omega$ is a bounded domain in $\Bbb R^d$ and $(0,T)$ is the time interval. We first prove the Lipschitz stability in $\Omega \times (\varepsilon, T-\varepsilon)$ with given $\varepsilon>0$ for the determination of the solutions by Dirichlet data on arbitrarily chosen subboundary of $\partial\Omega$. Next we prove the Lipschitz stability for an inverse problem of determining spatially varying factors of source terms and a coefficient by extra boundary data and spatial data at intermediate time.
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cs.LG 1years
2025 1verdicts
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Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems
GPPI-AS offers a mesh-free, closed-form policy iteration solver for HJB and mean field game forward and inverse problems, with Schwarz acceleration cutting iterations roughly in half.