无需物理定律先验，用跨模态传感器数据训练通用物理信号模型。
A Phenomenological AI Foundation Model for Physical Signals
- 基于现象学框架，用0.59亿条跨模态传感器数据训练模型。
- 模型可预测未见物理现象，从弹簧振子到电网动态均有效。
- 适合做多物理领域统一建模的研究者与工业界应用者。
本研究旨在构建一个能跨多种现象、领域、应用场景及传感设备的物理信号人工智能基础模型。我们提出一种现象学方法与框架，用于创建和验证此类基础模型。基于该框架，我们在包含0.59亿个样本的跨模态传感器测量数据上训练了模型，涵盖电流传导、流体流动到光学传感器等多种信号类型。值得注意的是，模型训练过程中未引入任何物理定律或归纳偏置。通过多个真实世界实验，我们证明单一基础模型能够有效编码并预测机械运动、热力学等物理行为，包括训练中未见过的现象。模型还能在不同复杂度的物理过程中扩展应用，从简单弹簧-质量系统的轨迹追踪到大型电力系统动态预测。本工作展示了构建统一物理世界过程人工智能基础模型的巨大潜力。
原文摘要 · Abstract (English)
The objective of this work is to develop an AI foundation model for physical signals that can generalize across diverse phenomena, domains, applications, and sensing apparatuses. We propose a phenomenological approach and framework for creating and validating such AI foundation models. Based on this framework, we developed and trained a model on 0.59 billion samples of cross-modal sensor measurements, ranging from electrical current to fluid flow to optical sensors. Notably, no prior knowledge of physical laws or inductive biases were introduced into the model. Through several real-world experiments, we demonstrate that a single foundation model could effectively encode and predict physical behaviors, such as mechanical motion and thermodynamics, including phenomena not seen in training. The model also scales across physical processes of varying complexity, from tracking the trajectory of a simple spring-mass system to forecasting large electrical grid dynamics. This work highlights the potential of building a unified AI foundation model for diverse physical world processes.
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