Path integral analysis of multiplicative fractional Gaussian noise yields a Gaussian propagator via Lamperti transform and reveals an effective drift causing probability buildup in low-noise regions under confinement.
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A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 systems.
Branching path statistics are cast into Navier-Stokes nonlinear transport to produce new propagator representations and backward Monte Carlo algorithms for confined fluid flows.
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Confined kinetics and heterogeneous diffusion driven by fractional Gaussian noise: A path integral approach
Path integral analysis of multiplicative fractional Gaussian noise yields a Gaussian propagator via Lamperti transform and reveals an effective drift causing probability buildup in low-noise regions under confinement.
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Physics and causally constrained discrete-time neural models of turbulent dynamical systems
A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 systems.
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Branching Paths Statistics for confined Flows : Adressing Navier-Stokes Nonlinear Transport
Branching path statistics are cast into Navier-Stokes nonlinear transport to produce new propagator representations and backward Monte Carlo algorithms for confined fluid flows.