Inferring microscale properties of interacting systems from macroscale observations

Campioni, Nazareno; Husmeier, Dirk; Morales, Juan Manuel; Gaskell, Jennifer; Torney, Colin J.

doi:10.1103/physrevresearch.3.043074

Cited by 1 publication

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stochastic differential equations are particularly expressive dynamical models naturally fit for representing systems evolving on multiple time-scales [1][2][3][4]. Extracting stochastic evolution equations from such systems has been of major interest in most sciences [5][6][7][8][9][10][11][12][13][14][15]. While identification of continuous time deterministic models has been largely resolved in the past [16][17][18][19], the same is not true for their stochastic counterparts.…”

Section: Introductionmentioning

confidence: 99%

Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems

Maoutsa¹

2023

Preprint

View full text Add to dashboard Cite

Stochastic evolution equations describing the dynamics of systems under the influence of both deterministic and stochastic forces are prevalent in all fields of science. Yet, identifying these systems from sparse-in-time observations remains still a challenging endeavour. Existing approaches focus either on the temporal structure of the observations by relying on conditional expectations, discarding thereby information ingrained in the geometry of the system's invariant density; or employ geometric approximations of the invariant density, which are nevertheless restricted to systems with conservative forces. Here we propose a method that reconciles these two paradigms. We introduce a new data-driven path augmentation scheme that takes the local observation geometry into account. By employing non-parametric inference on the augmented paths, we can efficiently identify the deterministic driving forces of the underlying system for systems observed at low sampling rates. * https://dimitra-maoutsa.gitlab.io/ 2 Here we employ the term path augmentation to refer to what is widely known as data augmentation. We resorted to this term because we consider it as more elegant and better descriptive of the proposed augmentation process, while the term 'data augmentation' is considerably vague.

show abstract

Section: Introductionmentioning

confidence: 99%

Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems

Maoutsa¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

Inferring microscale properties of interacting systems from macroscale observations

Cited by 1 publication

References 33 publications

Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems

Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems

Contact Info

Product

Resources

About