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
基本信息
- 批准号:2211908
- 负责人:
- 金额:$ 60万
- 依托单位:
- 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:
- 关键词:
项目摘要
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(PDEs)来建模的。由于通过传统的数值方法求解这些偏微分方程非常昂贵,因此越来越多地使用诸如深度神经网络(dnn)之类的仿真器来近似偏微分方程解。虽然在这个方向上付出了巨大的努力,但现有的技术提供了昂贵的解决方案,这些解决方案不能跨不同的应用程序转移,也不能扩展到复杂的pde。本项目旨在使用分而治之的方法解决这些限制。在将为该项目开发的框架中,该项目将首先建立一个dnn库,用于解决称为基因组的小域上的单物理PDE系统。然后,为了解决大型看不见的域上的多物理场偏微分方程,该项目将开发一种自适应方法,将dnn耦合并组装它们的基因组预测,以便在整个域内满足控制方程。该项目预计,预训练的dnn和耦合机制将极大地造福那些无法获得科学机器学习所需的硬件或知识的学者。该框架的可转移性有可能减少与使用深度神经网络解决偏微分方程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带来巨大利益。该项目将构建使用贝叶斯强化学习(BRL)进行泛化的学会基因组算子(LEGOs),即:(1)模拟多物理场系统,和/或(2)实现时空可转移性和可扩展性。这项工作的贡献有望通过预训练的dnn实现固体和流体行为的实时近似,从而消除长时间的训练时间,同时提高准确性和可扩展性。LEGO框架是假设驱动的,并利用了独特利用并行和异构机器的领域分解方法的数学。该框架首先将具有任意初始和边界条件的大域分解为称为基因组的小子域,从而解决了PDE系统。然后,通过预训练的乐高来近似每个基因组的解决方案,这样基因组预测的组装就可以近似大域的解决方案。从本质上讲,乐高模型在基因组中模拟不同的物理现象(例如,材料变形或流体流动),而BRL代理将乐高模型耦合以模拟多物理现象和/或在时空上扩展乐高模型的预测,同时保持整个基因组的溶液一致性。为了实现具有高可移植性和可扩展性的实时性和鲁棒性,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)从数学上估计解组装过程中的传播误差。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 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:
- 影响因子:7.2
- 作者:Foumani,Zahra Zanjani;Shishehbor,Mehdi;Bostanabad,Ramin
- 通讯作者:Bostanabad,Ramin
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ramin Bostanabad其他文献
Simultaneous and meshfree topology optimization with physics-informed Gaussian processes
基于物理信息的高斯过程的同步无网格拓扑优化
- DOI:
10.1016/j.cma.2024.117698 - 发表时间:
2025-03-15 - 期刊:
- 影响因子:7.300
- 作者:
Amin Yousefpour;Shirin Hosseinmardi;Carlos Mora;Ramin Bostanabad - 通讯作者:
Ramin Bostanabad
Data Centric Design: A New Approach to Design of Microstructural Material Systems
- DOI:
10.1016/j.eng.2021.05.022 - 发表时间:
2022-03-01 - 期刊:
- 影响因子:11.600
- 作者:
Wei Chen;Akshay Iyer;Ramin Bostanabad - 通讯作者:
Ramin Bostanabad
Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments
激光粉末床熔合中加工-性能关系的揭示:机器学习与高通量实验的协同作用
- DOI:
10.1016/j.matdes.2025.113705 - 发表时间:
2025-04-01 - 期刊:
- 影响因子:7.900
- 作者:
Mahsa Amiri;Zahra Zanjani Foumani;Penghui Cao;Lorenzo Valdevit;Ramin Bostanabad - 通讯作者:
Ramin Bostanabad
Operator learning with Gaussian processes
- DOI:
10.1016/j.cma.2024.117581 - 发表时间:
2025-02-01 - 期刊:
- 影响因子:
- 作者:
Carlos Mora;Amin Yousefpour;Shirin Hosseinmardi;Houman Owhadi;Ramin Bostanabad - 通讯作者:
Ramin Bostanabad
Ramin Bostanabad的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Ramin Bostanabad', 18)}}的其他基金
CAREER: Design Under Uncertainty in Combinatorially Expanding Spaces
职业:组合扩展空间的不确定性下的设计
- 批准号:
2238038 - 财政年份:2023
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
CRII:OAC: Machine Learning- Enhanced Multiscale Simulation of Fiber Composites
CRII:OAC:机器学习 - 纤维复合材料的增强多尺度模拟
- 批准号:
2103708 - 财政年份:2021
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
相似国自然基金
胆固醇羟化酶CH25H非酶活依赖性促进乙型肝炎病毒蛋白Core及Pre-core降解的分子机制研究
- 批准号:82371765
- 批准年份:2023
- 资助金额:50 万元
- 项目类别:面上项目
锕系元素5f-in-core的GTH赝势和基组的开发
- 批准号:22303037
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
基于合成致死策略搭建Core-matched前药共组装体克服肿瘤耐药的机制研究
- 批准号:
- 批准年份:2022
- 资助金额:52 万元
- 项目类别:
鼠伤寒沙门氏菌LPS core经由CD209/SphK1促进树突状细胞迁移加重炎症性肠病的机制研究
- 批准号:
- 批准年份:2022
- 资助金额:30 万元
- 项目类别:青年科学基金项目
基于外泌体精准调控的“核-壳”(core-shell)同步血管化骨组织工程策略的应用与机制探讨
- 批准号:
- 批准年份:2020
- 资助金额:55 万元
- 项目类别:
肌营养不良蛋白聚糖Core M3型甘露糖肽的精确制备及功能探索
- 批准号:92053110
- 批准年份:2020
- 资助金额:70.0 万元
- 项目类别:重大研究计划
Core-1-O型聚糖黏蛋白缺陷诱导胃炎发生并介导慢性胃炎向胃癌转化的分子机制研究
- 批准号:81902805
- 批准年份:2019
- 资助金额:20.5 万元
- 项目类别:青年科学基金项目
原始地球增生晚期的Core-merging大碰撞事件:地核增生、核幔平衡与核幔边界结构的新认识
- 批准号:41973063
- 批准年份:2019
- 资助金额:65.0 万元
- 项目类别:面上项目
RBM38通过协助Pol-ε结合、招募core调控HBV复制
- 批准号:31900138
- 批准年份:2019
- 资助金额:24.0 万元
- 项目类别:青年科学基金项目
CORDEX-CORE区域气候模拟与预估研讨会
- 批准号:41981240365
- 批准年份:2019
- 资助金额:1.5 万元
- 项目类别:国际(地区)合作与交流项目
相似海外基金
Collaborative Research: OAC CORE: Federated-Learning-Driven Traffic Event Management for Intelligent Transportation Systems
合作研究:OAC CORE:智能交通系统的联邦学习驱动的交通事件管理
- 批准号:
2414474 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
合作研究:OAC Core:用于大规模时空预测的分布式图学习网络基础设施
- 批准号:
2403312 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
Collaborative Research: Enabling Cloud-Permitting and Coupled Climate Modeling via Nonhydrostatic Extensions of the CESM Spectral Element Dynamical Core
合作研究:通过 CESM 谱元动力核心的非静水力扩展实现云允许和耦合气候建模
- 批准号:
2332469 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
Using Sex-reversed Chickens To Identify Core Spermatogenic Regulatory Genes
使用性别逆转鸡来鉴定核心生精调节基因
- 批准号:
BB/Y005465/1 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Research Grant
Collaborative Research: Stanford-Florida Program in Support of LIGO on Coatings and Core Optics
合作研究:斯坦福-佛罗里达计划支持 LIGO 涂层和核心光学器件
- 批准号:
2309086 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
OAC Core: Cost-Adaptive Monitoring and Real-Time Tuning at Function-Level
OAC核心:功能级成本自适应监控和实时调优
- 批准号:
2402542 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
OAC Core: OAC Core Projects: GPU Geometric Data Processing
OAC 核心:OAC 核心项目:GPU 几何数据处理
- 批准号:
2403239 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
Collaborative Research: SaTC: CORE: Medium: Using Intelligent Conversational Agents to Empower Adolescents to be Resilient Against Cybergrooming
合作研究:SaTC:核心:中:使用智能会话代理使青少年能够抵御网络诱骗
- 批准号:
2330940 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant
Developing Teaching Tools to Promote Transfer of Core Concept Knowledge Across Biological Scales and Sub-disciplines.
开发教学工具以促进跨生物尺度和子学科的核心概念知识的转移。
- 批准号:
2336776 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Standard Grant
SaTC: CORE: Small: An evaluation framework and methodology to streamline Hardware Performance Counters as the next-generation malware detection system
SaTC:核心:小型:简化硬件性能计数器作为下一代恶意软件检测系统的评估框架和方法
- 批准号:
2327427 - 财政年份:2024
- 资助金额:
$ 60万 - 项目类别:
Continuing Grant