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物理信息卷积神经网络在多孔介质流体流动中的应用Physics-informed convolutional neural networks for fluid flow through porous media
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
最近人工智能的进展促成了一种新框架，该框架利用物理信息卷积神经网络（CNN）预测多孔介质中流体流动的孔级速度场。这种创新方法采用卷积编码器-解码器架构，不仅提高了预测的准确性，还显著减少了解算器收敛所需的时间。将基于物理的原则融入神经网络设计，使得对多孔结构中复杂流体动力学的建模更加可靠，这对工程和环境科学的各种应用至关重要。
Recent advancements in artificial intelligence have led to the development of a novel framework that employs physics-informed convolutional neural networks (CNNs) to predict pore-scale velocity fields in fluid flow through porous media. This innovative approach utilizes a convolutional encoder-decoder architecture, which not only enhances the accuracy of predictions but also significantly reduces the time required for solver convergence. The integration of physics-based principles into the neural network design allows for more reliable modeling of complex fluid dynamics in porous structures, which is crucial for various applications in engineering and environmental science.
要点
新框架提高了多孔介质中流体流动的预测准确性。
The new framework improves prediction accuracy for fluid flow in porous media.
利用卷积编码器-解码器架构可以减少解算器收敛时间。
Utilizing a convolutional encoder-decoder architecture can reduce solver convergence times.
该方法整合了基于物理的原则，增强了AI模型的可靠性。
The approach integrates physics-based principles, enhancing the reliability of AI models.
为何重要
这一发展代表了人工智能与流体动力学交叉领域的重要进展，可能会改变工程师和科学家在各个领域对多孔介质的建模和分析方式。
This development represents a significant step forward in the intersection of AI and fluid dynamics, potentially transforming how engineers and scientists model and analyze porous media in various fields.
产品经理
对于产品经理而言，这一创新可能会导致依赖流体动力学的行业（如石油和天然气、环境工程和材料科学）中更高效的产品开发周期。
For product managers, this innovation could lead to more efficient product development cycles in industries reliant on fluid dynamics, such as oil and gas, environmental engineering, and materials science.
开发者
开发人员和工程师应考虑如何将物理信息AI技术整合到他们的仿真和建模工具中，以提高其稳健性和效率。
Developers and engineers should consider how integrating physics-informed AI techniques can improve the robustness and efficiency of their simulations and modeling tools.
学生与求职者
在AI和工程领域的学生和求职者应认识到，结合机器学习与传统科学原则的跨学科方法日益重要，从而增强他们未来职业机会的技能。
Students and job-seekers in AI and engineering fields should recognize the growing importance of interdisciplinary approaches that combine machine learning with traditional scientific principles, enhancing their skill sets for future career opportunities.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
Physics-informed convolutional neural networks for fluid flow through porous mediaResearchers developed a neural-network-based framework that predicts pore-scale velocity fields in fluid flow through porous media, utilizing a convolutional encoder-decoder architecture to enhance prediction accuracy and reduce solver convergence times.