A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
A new cooperative localization algorithm based on overlapping covariance intersection is fully distributed, provably recursively consistent, and scalable to ultra large-scale multi-agent systems without performance loss from ignored cross-correlations.
Neural surrogates enable a four-stage alternating algorithm for nonlocal mean-field Schrödinger bridges with linear scaling and Gronwall stability bounds.
Existence is proved for solutions of nonlinear stationary Kolmogorov equations with partially degenerate diffusion and discontinuous coefficients using a Lyapunov integral condition and projection regularity.
Causal PDE-Control Models combine causal drivers with PDE control and filtering to deliver interpretable dynamic portfolio rules that outperform benchmarks in Sharpe ratio and turnover on U.S. equity data.
citing papers explorer
-
Consistent Distributed Cooperative Localization for Ultra Large-Scale Multi-agent Systems
A new cooperative localization algorithm based on overlapping covariance intersection is fully distributed, provably recursively consistent, and scalable to ultra large-scale multi-agent systems without performance loss from ignored cross-correlations.