A particle scheme based on implicit Euler time stepping and spatial sampling is proved to converge for first-order MFGs under displacement monotonicity, handling non-separable Hamiltonians and singular data for arbitrary horizons.
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A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
citing papers explorer
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Numerical analysis of first-order mean field games under displacement monotonicity
A particle scheme based on implicit Euler time stepping and spatial sampling is proved to converge for first-order MFGs under displacement monotonicity, handling non-separable Hamiltonians and singular data for arbitrary horizons.
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Neural Parameter Calibration for Finite-State Mean Field Games
A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.