学习随机动态,并使用变压器预测新出现的行为
Corneel Casert1,2, Isaac Tamblyn3,4,5, Stephen Whitelam6
1Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA. ccasert@lbl.gov.
Nature communications
|February 29, 2024
概括
一个变压器神经网络从单个轨迹中学习了复杂的系统动态. 这种人工智能模型准确地预测了在未见的条件下出现的行为,包括相位过渡.
科学领域:
- 物理 物理学 物理
- 人工智能的人工智能
- 复杂的系统复杂的系统.
背景情况:
- 随机系统和活性物质表现出复杂的动态.
- 在这些系统中预测新出现的行为往往需要详细了解底层规则.
- 神经网络,特别是变压器,在学习复杂模式方面表现有前途.
研究的目的:
- 为了研究一个变压器神经网络是否可以从有限的数据中学习一个随机系统的动态规则.
- 评估受过训练的网络在新条件下对新出现的行为进行预测的能力.
- 探索变压器在理解复杂物理系统中的应用.
主要方法:
- 在活性物质格子模型的单个轨迹上训练变压器神经网络.
- 模拟活性物质模型在特定密度下,它形成分散的集群.
- 评估变压器在训练过程中未遇到的密度下对系统行为的预测.
主要成果:
- 变压器成功地学习了随机系统的基本动态规则.
- 经过训练的网络准确地预测了新密度的运动性诱导相位分离.
- 该模型展示了能够表示众多和非局部动态规则的能力.
结论:
- 变压器可以从观测数据中学习复杂的系统动态,即使只有一条轨迹.
- 这种方法可以在未见的条件下准确预测新出现的行为和相位过渡.
- 该方法提供了一种灵活的方法来研究各种物理系统,而无需明确的速率计数或粗粒度.
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