Department of Mechanical Engineering, Iowa State University, Ames IA-50014, United States.
Department of Mechanical Engineering, Iowa State University, Ames IA-50014, United States.
Neural Netw. 2018 Feb;98:305-317. doi: 10.1016/j.neunet.2017.12.003. Epub 2017 Dec 18.
Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element.
递归神经网络（RNN）和长短期记忆（LSTM）网络是利用序列信息的常用架构，其中输出依赖于输入序列。然而，在大多数考虑的问题中，依赖关系通常存在于潜在域中，这可能不适用于涉及仅在可见域中依赖先前状态且具有已知终端状态的逐步转换序列预测的应用。我们提出了卷积神经网络（CNN）和堆叠自动编码器（SAE）的混合架构，以学习一系列因果动作，将输入视觉模式或分布非线性地转换为具有相同支持的目标视觉模式或分布，并在涉及流体物理的实际工程问题中证明了其实用性。我们解决了一个涉及微流控流动造型的高维一对多逆映射问题，其中很少探索使用深度学习方法作为逆映射。这项工作为应用科学家和工程师提供了一个有益的用例，说明了深度学习如何成为解决高维物理问题的有益方法，并可能为材料科学和医学生物学等领域带来有影响力的进展，其中多步拓扑变换是一个关键要素。
