Building General Langevin Models from Discrete Datasets
Federica Ferretti, Victor Chardès, Thierry Mora, Aleksandra M. Walczak, Irene Giardina
Abstract
A new technique for extracting equations of motion from data opens the way for the application of a robust inference apparatus to a class of widely used models to describe stochastic dynamics in physics and biophysics.
Topics & Concepts
Computer scienceInferenceStatistical physicsClass (philosophy)Langevin dynamicsMotion (physics)Dynamics (music)AlgorithmStochastic processStatistical inferenceTheoretical computer scienceArtificial intelligenceLangevin equationStochastic modellingApplied mathematicsNoisy dataEquations of motionEstimation theoryMathematical modelNew classStochastic differential equationUncertainty quantificationMarkov Chains and Monte Carlo MethodsGaussian Processes and Bayesian InferenceMicro and Nano Robotics