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A full-density approach to simulating random iteration equations with applications
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DOI：10.1016/j.cnsns.2026.110508.png)
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Abstract
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• Presents a new computational modeling framework for random iteration equations. • Avoids pathwise Monte Carlo simulations, applies iterative full-density propagation. • High simulation flexibility including discontinuities and non-standard densities. • Broad utility: Applied to RDEs and SDEs, chaotic maps, and global optimization. • Introduces a new Full-Density Gradient Descent (FDGD) optimization approach.
Keywords:
Random iteration equation
Density propagation
Uncertainty quantification
Nonlinear dynamics
Full-density gradient descent
Stochastic differential equation
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