A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
Springer Briefs in Mathematics, 2013
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Nonsmooth extension of the Brezzi-Rappaz-Raviart approximation theorem via metric regularity, applied to quasi-optimal finite-element error estimates for viscous Hamilton-Jacobi equations and second-order mean field games.
citing papers explorer
-
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.
-
A nonsmooth extension of the Brezzi-Rappaz-Raviart approximation theorem via metric regularity techniques and applications to nonlinear PDEs
Nonsmooth extension of the Brezzi-Rappaz-Raviart approximation theorem via metric regularity, applied to quasi-optimal finite-element error estimates for viscous Hamilton-Jacobi equations and second-order mean field games.