A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
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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.
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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.
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Existence theorems for nonlinear stationary Kolmogorov equations with partially degenerate diffusion matrices
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.
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Causal PDE-Control Models for Dynamic Portfolio Optimization with Latent Drivers
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.