Pith. sign in

REVIEW 2 cited by

Benchmark control problems in nonequilibrium statistical mechanics

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2506.15122 v2 pith:3BC2RQR5 submitted 2025-06-18 cond-mat.stat-mech

Benchmark control problems in nonequilibrium statistical mechanics

classification cond-mat.stat-mech
keywords nonequilibriumsystemsbenchmarkcontrolenoughmechanicsmethodsoptimization
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stochastic model, an optimization objective, and C++ and Python implementations that can be run on Unix-like systems. These benchmark systems are simple enough to run on a laptop, but challenging enough to test the capabilities of modern optimization methods. This release includes five problems and a worked example. The problem set is called NESTbench25, for NonEquilibrium STatistical mechanics benchmarks (2025).

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Shortcuts to state transitions for active matter

    cond-mat.stat-mech 2026-04 unverdicted novelty 7.0

    A geometric shortcut protocol using an auxiliary potential and geodesics on a dissipative-work metric enables finite-time state transitions in weakly active systems with reduced dissipation compared to linear ramps.

  2. A neural-network Maxwell's demon learns cold damping for work extraction

    cond-mat.stat-mech 2026-07 accept novelty 6.0

    A velocity-input neural Maxwell demon learns cold damping and extracts work near the theoretical bound from an underdamped thermal oscillator.