OAC Core: Geometry-aware and Deep Learning-based Cyberinfrastructure for Scalable Modeling of Solids and Fluids

OAC 核心：基于几何感知和深度学习的网络基础设施，用于固体和流体的可扩展建模

• 批准号: 2211908
• 负责人: Ramin Bostanabad
• 金额: $ 60万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211908&HistoricalAwards=false
• 关键词: OAC; Core; Geometry; aware; Deep

Many phenomena in solid and fluid mechanics are modeled via complex partial differential equations (PDEs). Since solving these PDEs via traditional numerical methods is prohibitively expensive, emulators such as deep neural networks (DNNs) are increasingly employed to approximate PDE solutions. While significant effort has been expended in this direction, existing technologies provide expensive solutions that are not transferable across different applications or scalable to complex PDEs. This project aims to address these limitations using a divide and conquer approach. In the framework that will be developed for the project, the project will first build a library of DNNs that solve single-physics PDE systems over small domains called genomes. Then, to solve multi-physics PDEs over large unseen domains, the project will develop an adaptive method that couples the DNNs and assembles their genome-wise predictions such that the governing equations are satisfied in the entire domain. The project expects that the pre-trained DNNs and coupling mechanism will greatly benefit scholars without access to the hardware or knowledge that are needed for scientific machine learning. The transferability of the framework has the potential to reduce the carbon footprint of the high computing costs that are associated with existing technologies that use DNNs to solve PDEs, providing great benefits to both scientific research and to society as a whole.The project will build LEarned Genomic Operators (LEGOs) that use Bayesian reinforcement learning (BRL) for generalization, i.e., for (1) emulating multi-physics systems, and/or (2) achieving spatiotemporal transferability and scalability. The contributions of this work are expected to enable on-the-fly approximation of the behavior of solids and fluids via pre-trained DNNs, thus eliminating long training times while increasing accuracy and scalability. The LEGO framework is hypothesis-driven and leverages the mathematics of domain decomposition methods that uniquely exploit parallel and heterogeneous machines. The framework solves a PDE system in a large domain with arbitrary initial and boundary conditions by first decomposing the domain into small subdomains called genomes. Then, the solution in each genome is approximated via pre-trained LEGOs such that the assembly of the genome-wise predictions approximates the solution in the large domain. In essence, the LEGOs model different physical phenomena (e.g., material deformation or fluid flow) in genomes while the BRL agent couples the LEGOs to model multi-physics phenomena and/or spatiotemporally extends the predictions of LEGOs while preserving solution consistency across the genomes. To achieve real-time and robust performance with high transferability and scalability, the framework (1) uses mixed-precision computing and hardware accelerators, (2) incorporates geometry-aware learning algorithms, and (3) mathematically estimates the propagated errors during solution assembly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

固体和流体力学中的许多现象都是通过复杂的偏微分方程（PDE）来建模的。由于通过传统的数值方法求解这些偏微分方程是非常昂贵的，仿真器，如深度神经网络（DNN）越来越多地被用来近似偏微分方程的解决方案。虽然在这方面已经花费了大量的努力，但现有技术提供了昂贵的解决方案，这些解决方案不能在不同的应用程序之间转移，也不能扩展到复杂的PDE。该项目旨在使用分而治之的方法来解决这些限制。在为该项目开发的框架中，该项目将首先构建一个DNN库，用于解决称为基因组的小域上的单物理PDE系统。然后，为了解决大型未知域上的多物理偏微分方程，该项目将开发一种自适应方法，该方法将DNN耦合并组装其基因组预测，以便在整个域中满足控制方程。该项目预计，预先训练的DNN和耦合机制将大大有利于学者，而无需访问科学机器学习所需的硬件或知识。该框架的可移植性有可能减少与使用DNN解决PDE的现有技术相关的高计算成本的碳足迹，为科学研究和整个社会提供巨大利益。该项目将构建使用贝叶斯强化学习（BRL）进行泛化的学习基因组算子（LEARNed Genomic Operators，LEARNed Genomic Operators），即，用于（1）仿真多物理场系统，和/或（2）实现时空可转移性和可缩放性。这项工作的贡献预计将通过预先训练的DNN实现对固体和流体行为的动态近似，从而消除长时间的训练，同时提高准确性和可扩展性。乐高框架是假设驱动的，并利用数学的域分解方法，唯一地利用并行和异构机。该框架解决了一个PDE系统在一个大的域与任意的初始和边界条件，首先分解成小的子域称为基因组的域。然后，每个基因组中的解决方案通过预训练的LECSTR来近似，使得基因组预测的组合近似于大域中的解决方案。本质上，LEDGE模拟不同的物理现象（例如，材料变形或流体流动），而BRL代理将LEQs耦合到多物理现象建模和/或时空扩展LEQs的预测，同时保持跨基因组的解决方案一致性。为了实现具有高可移植性和可扩展性的实时和鲁棒性能，该框架（1）使用混合精度计算和硬件加速器，（2）结合几何感知学习算法，以及（3）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis

并发多尺度损伤分析中的自适应时空降维

• DOI: 10.1007/s00466-023-02299-7
• 发表时间: 2023
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Deng, Shiguang;Apelian, Diran;Bostanabad, Ramin
• 通讯作者: Bostanabad, Ramin

Multi-fidelity cost-aware Bayesian optimization

• DOI: 10.1016/j.cma.2023.115937
• 发表时间: 2023-02-20
• 期刊: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
• 影响因子: 7.2
• 作者: Foumani，Zahra Zanjani;Shishehbor，Mehdi;Bostanabad，Ramin
• 通讯作者: Bostanabad，Ramin