Data-driven non-Markovian closure models

Markovian Models for Sequential Data

Hidden Markov Models HMMs are statistical models of sequential data that have been used successfully in many machine learning applications especially for speech recognition Further more in the last few years many new and promising probabilistic models related to HMMs have been proposed We rst summarize the basics of HMMs and then review several recent related learning algorithms and extensions ...

Non-Markovian closure kinetics of flexible polymers with hydrodynamic interactions.

This paper presents a theoretical analysis of the closure kinetics of a polymer with hydrodynamic interactions. This analysis, which takes into account the non-Markovian dynamics of the end-to-end vector and relies on the preaveraging of the mobility tensor (Zimm dynamics), is shown to reproduce very accurately the results of numerical simulations of the complete nonlinear dynamics. It is found...

Non-discounted Denumerable Markovian Decision Models

Intrinsic scales for high-dimensional LEVY-driven models with non-Markovian synchronizing updates

We propose stochastic N -component synchronization models (x1(t), . . . , xN (t)), xj ∈ Rd, t ∈ R+, whose dynamics is described by Lévy processes and synchronizing jumps. We prove that symmetric models reach synchronization in a stochastic sense: differences between components d (N) kj (t) = xk(t)−xj(t) have limits in distribution as t → ∞. We give conditions of existence of natural (intrinsic)...

long lead runoff simulation using data driven models

runoff simulation is a vital issue in water resource planning and management. various models with different levels of accuracy and precision are developed for this purpose considering various prediction time scales. in this paper, two models of ihacres (identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data) and ann (artificial neural network) mode...