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Benchmark control problems in nonequilibrium statistical mechanics

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

Stephen Whitelam , Corneel Casert , Megan Engel , Isaac Tamblyn This is my paper · ORCID
classification cond-mat.stat-mech
keywords nonequilibriumsystemsbenchmarkcontrolenoughmechanicsmethodsoptimization
verification ladder T0 review T1 audit T2 compute T3 formal
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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).

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Cited by 2 Pith papers

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

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

    cond-mat.stat-mech 2026-07 accept novelty 6.0 of 10

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

  2. Optimal transport and control of an active particle near a plane wall

    cond-mat.soft 2026-03 conditional novelty 5.0 of 10

    Near a no-slip wall, the minimum-work optical-trap protocol for transporting an active particle away from the wall breaks time-reversal symmetry with the return protocol.

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