用主动学习提升物理模拟代理模型的训练效率
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning
- 在线生成数据并聚焦困难参数区域训练
- 2D热方程测试中提升泛化能力，降低计算开销
- 适合需要高效训练多参数物理模型的研究者
人工智能正通过深度神经网络代理模型改变科学计算，这些模型可近似求解偏微分方程（PDE）。传统离线训练需预先用数值求解器生成全部数据，存在存储与输入输出效率问题。我们此前提出的Melissa框架通过“按需生成”并流式传输数据，缓解了这一问题。本文提出新方法Breed，结合基于训练损失统计的自适应多重重要性采样，实现主动学习，提升在线代理训练的数据效率。该代理模型为直接、多参数形式，可直接预测不同初始与边界条件下的特定时间步解。初步实验在二维热方程上验证了Breed的有效性：显著提升代理模型泛化能力，同时减少计算开销。
原文摘要 · Abstract (English)
Artificial intelligence is transforming scientific computing with deep neural network surrogates that approximate solutions to partial differential equations (PDEs). Traditional off-line training methods face issues with storage and I/O efficiency, as the training dataset has to be computed with numerical solvers up-front. Our previous work, the Melissa framework, addresses these problems by enabling data to be created "on-the-fly" and streamed directly into the training process. In this paper we introduce a new active learning method to enhance data-efficiency for on-line surrogate training. The surrogate is direct and multi-parametric, i.e., it is trained to predict a given timestep directly with different initial and boundary conditions parameters. Our approach uses Adaptive Multiple Importance Sampling guided by training loss statistics, in order to focus NN training on the difficult areas of the parameter space. Preliminary results for 2D heat PDE demonstrate the potential of this method, called Breed, to improve the generalization capabilities of surrogates while reducing computational overhead.
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