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English(EN) Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces

新的Functional Adjoint Sampler可在无限维度空间中实现可扩展采样

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新的方法——Functional Adjoint Sampler (FAS)，用于从无限维度函数空间中的Gibbs分布进行采样。该技术基于Adjoint Sampling，并利用随机最优控制理论来高效模拟扩散过程的轨迹，特别是在罕见事件或边界约束的情况下。FAS在合成势能和真实分子系统（如Alanine Dipeptide和Chignolin）的过渡路径采样方面表现出卓越的性能。 AI

影响 这种新的采样方法有望改进分子动力学和其他复杂系统的模拟，从而可能加速药物发现和材料科学等领域的研究。

排序理由 该集群描述了一篇关于新采样方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新的Functional Adjoint Sampler可在无限维度空间中实现可扩展采样

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该集群描述了一篇关于新采样方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Byoungwoo Park, Juho Lee, Guan-Horng Liu ·

    Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces

    arXiv:2511.06239v2 Announce Type: replace Abstract: Learning-based methods for sampling from the Gibbs distribution in finite-dimensional spaces have progressed quickly, yet theory and algorithmic design for infinite-dimensional function spaces remain limited. This gap persists d…

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