用强化学习动态选样，让神经网络更高效满足物理约束。
Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
- 用强化学习自动优化采样策略，随训练过程动态调整。
- 在测试问题上显著提升约束满足度，训练效率更高。
- 适合需自适应采样的场景，如物理模型与稳定性验证。
在连续域上训练满足普遍约束的神经网络面临独特挑战。典型例子包括李雅普诺夫神经网络（Lyapunov NNs）和物理信息神经网络（PINNs），其解析解通常不可得或过于严格。因此常采用基于样本的方法来强制约束，而采样选择对收敛速度、稳定性与解的质量有显著影响。现有方法多依赖固定启发式规则或手工设计，实际效果不佳。本文提出通过数据和经验学习如何动态、迭代地调整样本以响应模型学习进展。该方法利用强化学习训练采样策略，在测试问题上显著提升约束满足的实证表现，同时大幅提高训练效率。我们在李雅普诺夫神经网络和物理信息神经网络上验证了该方法，并展示了其在自适应输入选择至关重要的其他领域中的广泛适用性。
原文摘要 · Abstract (English)
Training neural networks to satisfy universal constraints over continuous domains poses unique challenges. Common examples include Lyapunov Neural Networks (Lyapunov NNs) and Physics-Informed Neural Networks (PINNs), where analytical solutions are generally either unavailable or overly restrictive. Sample-based methods are therefore commonly used to enforce these constraints, and the choice of samples has a substantial impact on convergence speed, stability, and solution quality. Most existing methods rely on fixed heuristics or handcrafted rules, and are suboptimal in practice. In this paper, we aim to improve upon them by learning, from data and experience, how to dynamically and iteratively adjust the samples in response to the model's evolving learning performance. Trained by reinforcement learning, the learned policy improves empirical constraint satisfaction on test problems while significantly improving efficiency. We validate the approach on both Lyapunov NNs and PINNs, and demonstrate its broader applicability to domains where adaptive input selection is essential for effective training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。