CoMetaPNS：心脏电生理模拟的持续元学习个性化神经代理
CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations
AI总结：
提出持续元学习框架CoMetaPNS，通过贝叶斯高斯混合模型记忆缓冲区分辨数据来源，实现个性化神经代理的持续学习，避免灾难性遗忘，在心脏模拟预测中优于基线。
摘要：
个性化虚拟心脏模拟面临模型个性化和计算成本的挑战。虽然神经代理提供了最先进的解决方案，但它们通常只解决高效个性化或训练可泛化模型中的一个方面。最近的工作通过使用有限的主题特定上下文数据，通过小样本生成建模与集合条件代理和元学习摊销推理，重新定义了学习个性化代理的过程。然而，这些方法假设一个静态且多样化的训练分布，并具有已知的任务标识符。当新数据可用时，它们需要与所有先前数据一起进行昂贵的重新训练，以避免灾难性遗忘——即模型在训练新任务时忘记旧任务的现象。这在临床环境中是一个主要限制，因为未标记的数据通常顺序到达，而完全重新训练是不可行的。本文提出了一种新的持续元学习框架，以实现个性化的神经代理，该代理不仅能够持续整合信息，还能识别传入数据是否来自已知或未知的动态源。通过利用基于记忆缓冲区的持续贝叶斯高斯混合模型，我们的框架可以推断数据随时间变化的标识符和关系——这是有效元学习所必需的。在合成心脏数据上的实验结果表明，与现有基线相比，我们的方法在模拟预测、计算可扩展性和对灾难性遗忘的鲁棒性方面表现更优。
英文摘要：
Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically address either efficient personalization or training generalizable models. Recent work reframes this by learning the process of personalizing a surrogate using limited subject-specific context data, through few-shot generative modeling with set-conditioned surrogates and meta-learned amortized inference. These methods, however, assume a static and diverse training distribution with known task identifiers. When new data becomes available, they require costly retraining with all prior data to avoid catastrophic forgetting - a phenomena where the model forgets earlier tasks when trained on new ones. This is a major limitation in clinical settings where often unlabeled data arrives sequentially and full retraining is infeasible. This paper presents a new continual meta-learning framework to achieve personalized neural surrogates able to not only continually integrate information but also identify whether incoming data stems from a known or unknown dynamics source. By leveraging a continual Bayesian Gaussian Mixture Model over a memory buffer, our framework can infer the identifiers and relationships of data over time - required for effective meta-learning. Empirical results on synthetic cardiac data demonstrate superior simulation forecasting, computational scalability, and resilience to catastrophic forgetting compared to existing baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
