English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transport coefficients, such as the mobility, thermal conductivity and shearviscosity, are quantities of prime interest in statistical physics. At themacroscopic level, transport coefficients relate an external forcing ofmagnitude $\eta$, with $\eta \ll 1$, acting on the system to an averageresponse expressed through some steady-state flux. In practice, steady-stateaverages involved in the linear response are computed as time averages over arealization of some stochastic differential equation. Variance reductiontechniques are of paramount interest in this context, as the linear response isscaled by a factor of $1/\eta$, leading to large statistical error. One way tolimit the increase in the variance is to allow for larger values of $\eta$ byincreasing the range of values of the forcing for which the nonlinear part ofthe response is sufficiently small. In theory, one can add an extra forcing tothe physical perturbation of the system, called synthetic forcing, as long asthis extra forcing preserves the invariant measure of the reference system. Theaim is to find synthetic perturbations allowing to reduce the nonlinear part ofthe response as much as possible. We present a mathematical framework forquantifying the quality of synthetic forcings, in the context of linearresponse theory, and discuss various possible choices for them. Our findingsare illustrated with numerical results in low-dimensional systems.

Extending the regime of linear response with synthetic forcings