Challenges and Opportunities for Machine Learning in Multiscale Computational Modeling
Challenges and Opportunities for Machine Learning in Multiscale Computational Modeling
复制标题
多尺度计算建模中机器学习的挑战和机遇
DOI：
10.1115/1.4062495
复制
发表时间：
2023
影响因子：
3.1
通讯作者：
Baek, Stephen
中科院分区：
文献类型：
--
作者：
Nguyen, Phong C.;Choi, Joseph B.;Udaykumar, H. S.;Baek, Stephen
许多机械工程应用需要多尺度计算建模和仿真。然而，解决复杂的多尺度系统仍然计算繁重，由于高维的解决方案空间。最近，机器学习（ML）已经成为一种很有前途的解决方案，可以作为传统数值方法的替代，加速或增强。开创性的工作已经证明，ML提供的解决方案，以控制系统的方程与那些使用直接数值方法获得的精度相当，但具有显着更快的计算速度。这些高速，高保真的估计可以通过提供一个更好的初始解决方案，传统的求解器，促进复杂的多尺度系统的解决方案。本文提供了一个关于使用ML进行复杂的多尺度建模和仿真的机会和挑战的视角。我们首先概述了当前用于模拟多尺度系统的最先进的ML方法，并强调了一些具有里程碑意义的发展。接下来，我们将讨论ML在多尺度计算建模中面临的挑战，例如数据和离散化依赖性，可解释性，数据共享和协作平台开发。最后，我们提出了未来几个潜在的研究方向。
Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data and discretization dependence, interpretability, and data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.