We adopt convolutional neural networks (CNN) to predict the basic properties
of the porous media. Two different media types are considered: one mimics the
sandstone, and the other mimics the systems derived from the extracellular
space of biological tissues. The Lattice Boltzmann Method is used to obtain the
labeled data necessary for performing supervised learning. We distinguish two
tasks. In the first, networks based on the analysis of the system's geometry
predict porosity and effective diffusion coefficient. In the second, networks
reconstruct the system's geometry and concentration map. In the first task, we
propose two types of CNN models: the C-Net and the encoder part of the U-Net.
Both networks are modified by adding a self-normalization module. The models
predict with reasonable accuracy but only within the data type, they are
trained on. For instance, the model trained on sandstone-like samples
overshoots or undershoots for biological-like samples. In the second task, we
propose the usage of the U-Net architecture. It accurately reconstructs the
concentration fields. Moreover, the network trained on one data type works well
for the other. For instance, the model trained on sandstone-like samples works
perfectly on biological-like samples.

本文采用卷积神经网络 (CNN) 来预测多孔介质的基本属性。该文分两项任务，第一项任务是利用几何分析来预测孔隙率和有效扩散系数，第二项任务则是通过重构系统的几何和浓度图来完成。其中，本文提出了两种不同种类的 CNN 模型，分别为 C-Net 和 U-Net。此外，U-Net 能够准确地重构浓度场，而且在各种数据类型上都表现出了良好的性能。

多孔介质扩散的深度学习

Deep learning for diffusion in porous media

Characterizing meltpool shape and geometry is essential in metal Additive
Manufacturing (MAM) to control the printing process and avoid defects.
Predicting meltpool flaws based on process parameters and powder material is
difficult due to the complex nature of MAM process. Machine learning (ML)
techniques can be useful in connecting process parameters to the type of flaws
in the meltpool. In this work, we introduced a comprehensive framework for
benchmarking ML for melt pool characterization. An extensive experimental
dataset has been collected from more than 80 MAM articles containing MAM
processing conditions, materials, meltpool dimensions, meltpool modes and flaw
types. We introduced physics-aware MAM featurization, versatile ML models, and
evaluation metrics to create a comprehensive learning framework for meltpool
defect and geometry prediction. This benchmark can serve as a basis for melt
pool control and process optimization. In addition, data-driven explicit models
have been identified to estimate meltpool geometry from process parameters and
material properties which outperform Rosenthal estimation for meltpool geometry
while maintaining interpretability.

本文引入了一个全面的机器学习框架，利用物理感知型的 MAM 特征化、多功能机器学习模型和评估指标，预测熔池缺陷和几何形状，为熔池控制和工艺优化提供了基础。此外，还发现了基于数据驱动的显式模型，用于从工艺参数和材料性质估算熔池几何形状。