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Machine learning solves complex optimal transport problems with Landauer boundary conditions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel machine learning approach to solve complex optimal transport problems for underdamped stochastic processes. This method is particularly effective in scenarios with non-linear boundary conditions, such as those related to the Landauer bit-erasure problem. The technique steers system dynamics from a single-peak to a double-peak distribution by manipulating an external potential, and its numerical results are benchmarked against a perturbative expansion suitable for the overdamped limit. AI

IMPACT Introduces a novel machine learning technique for complex physics simulations, potentially enabling new research avenues in statistical mechanics and control theory.

RANK_REASON The item is an academic paper published on arXiv detailing a new machine learning method for solving a specific class of physics/control theory problems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning solves complex optimal transport problems with Landauer boundary conditions

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The item is an academic paper published on arXiv detailing a new machine learning method for solving a specific class of physics/control theory problems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Julia Sanders, Dario Lucente, Marco Baldovin, Paolo Muratore-Ginanneschi ·

    Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution

    arXiv:2610.41599v1 Announce Type: cross Abstract: Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory. In this paper we develop a direct method, based on machine learning, to find the optimal solution of …

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