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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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6 results for “Onsager-Machlup”

Most probable paths for developed processesmath.PR

Develops methods to find most probable paths on complex manifolds.

problem Identifying optimal paths for manifold-valued processes, especially those with non-trivial structures.

method Constructs a general approach to defining and identifying most probable paths by measuring the Onsager-Machlup function on the anti-development of such processes.

result Derives explicit equations for development most probable paths that encompass various manifold-valued processes.

Onsager-Machlup function manifold-valued processes optimal paths stochastic processes Lie groups

2022-11-28abs ↗pdf ↗

Most probable paths for anisotropic Brownian motions on manifoldsmath.PR

Study of most probable paths for anisotropic Brownian motions on manifolds.

problem Characterizing paths of Brownian motions with anisotropic diffusion on manifolds.

method Using stochastic development and fiber bundle of linear frames, the study provides a comprehensive characterization of most probable paths.

result Explicit equations and integration methods for most probable paths on different geometries, including constant curvature surfaces.

Brownian motion manifolds anisotropic diffusion stochastic development Onsager-Machlup

2021-10-29abs ↗pdf ↗

Topological Hidden Markov Modelsstat.ME

Extends HMM to topological spaces for modeling complex data.

problem Modeling complex, continuous data in infinite-dimensional spaces.

method Use of Onsager-Machlup functional and Cameron-Martin space.

result Demonstrates versatility in identifying sleep states and snowfall patterns.

hidden markov model topological spaces Onsager-Machlup functional Cameron-Martin space sleep states

2022-05-26abs ↗pdf ↗

A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systemsmath.DS

Develops a machine learning framework for computing most probable paths in stochastic systems.

problem Computing the most probable paths in stochastic dynamical systems.

method Reformulates the boundary value problem of Hamiltonian systems and uses a neural network to solve the Euler-Lagrange equation for the Onsager-Machlup action functional.

result Demonstrates the efficacy and accuracy of the machine learning approach in computing most probable paths for stochastic systems with various types of noise.

Machine Learning Stochastic Dynamical Systems Most Probable Paths Onsager-Machlup Action Functional Neural Networks

2020-10-01abs ↗pdf ↗

Early warning indicators via latent stochastic dynamical systemsstat.ML

Novel approach detects early warning indicators in complex systems.

problem Detecting abrupt transitions in complex systems.

method Directed anisotropic diffusion map and latent stochastic dynamical systems.

result Early warning indicators can detect tipping points in state transitions.

early warning indicators latent stochastic dynamical systems complex systems tipping points brain diseases

2023-09-07abs ↗pdf ↗

Early warning prediction: Onsager-Machlup vs Schrödingerq-bio.QM

Study proposes a new early-warning framework for high-dimensional complex systems.

problem Predicting critical transitions in complex systems like epileptic seizures.

method Integrates manifold learning with stochastic dynamical system modeling, using Schrödinger bridge theory.

result Demonstrates higher sensitivity and robustness in epilepsy prediction.

early warning complex systems epilepsy stochastic differential equations Schrödinger bridge

2026-01-29abs ↗pdf ↗