Collaborative Research: OAC Core: Smart Surrogates for High Performance Scientific Simulations

合作研究：OAC Core：高性能科学模拟的智能替代品

• 批准号: 2212549
• 负责人: Shantenu Jha
• 金额: $ 20万
• 依托单位: Rutgers University New Brunswick
• 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: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212549&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Smart

High-fidelity computer simulations underpin discovery in a broad range of scientific domains. However, their computation cost limits their full potential. There have been increasing efforts in approximating scientific simulations with deep neural networks, to accelerate simulation workflows by orders of magnitude. Current practice, however, largely relies on fixed network architectures and offline simulation data -– predefined by experience, rather than optimized by quantitative metrics. This leads to an empirical, subjective, and laborious practice, yet with a suboptimal outcome. This research addresses the above critical gaps with a new conceptual, mathematical, and infrastructure framework for developing Smart Surrogates. As a domain-agnostic framework, Smart Surrogates will deliver timely support for an increasing but yet-to-be-met demand for surrogate modeling for scientific simulations. The prototype surrogates created in this project will also directly enable long-term follow-on research in each of the domains involved. This collaborative research provides multidisciplinary training at the intersection of artificial intelligence, high-performance computing, and scientific simulations in a variety of domains, helping prepare next-generation researchers adept at transdisciplinary thinking and skill. It plans to proactively recruit students from underrepresented groups, and develop a hands-on workshop on Smart Surrogates for dissemination to a broader student body. Finally, the dissemination of ROSE as an open-source toolkit will impact HPC simulation workflows in a broad range of social applications, including but not limited to drug design and the study of climate change.The development of Smart Surrogates includes three parallel but interwoven methodological, infrastructure, and domain evaluation thrusts: 1) Thrust I – Methodological Innovations: This thrust develops fundamental innovations in deep active learning to jointly optimizes training-data selection and neural architectures, in a Bayesian setting equipped with uncertainty quantification. This allows Smart Surrogates to support the intelligent active selection of training simulations along with dynamic adjustment of neural architectures; 2) Thrust II – Infrastructure innovations): This thrust designs, implements, and disseminates the RADICAL Optimal & Smart-Surrogate Explorer (ROSE) toolkit to support the concurrent and adaptive executions of simulation and surrogate training and selection tasks.; 3) Thrust III – Scientific innovations: This thrust grounds the developments and evaluation of Smart Surrogates in two domain problems: surrogates for 1) diffusion equations with singular initial conditions and 2) personalized virtual heart simulations, built on the team’s past works with established domain collaborators. This allows fast prototyping, while setting the basis for a continuum of follow-up research to adopt Smart Surrogates in a larger range of complex scientific simulations.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.

高保真计算机模拟支撑着广泛的科学领域的发现，然而，它们的计算成本限制了它们的全部潜力，人们越来越努力地利用深度神经网络来近似科学模拟，以将模拟工作流程加速几个数量级。然而，很大程度上依赖于固定的网络架构和离线模拟数据——由经验预定义，而不是通过定量指标优化，这导致了经验性、主观性和费力的实践，但本研究解决了上述关键问题。间隙与用于开发智能代理的新概念、数学和基础架构作为一个与领域无关的框架，智能代理将为科学模拟的代理建模日益增长但尚未满足的需求提供及时的支持。该项目还将直接促进各个相关领域的长期后续研究，该合作研究在人工智能、高性能计算和科学模拟等多个领域的交叉领域提供多学科培训，帮助为下一步做好准备。一代研究人员它计划主动从代表性不足的群体中招募学生，并开发一个关于智能代理的实践研讨会，以向更广泛的学生群体传播。最后，ROSE 作为开源工具包的传播将影响 HPC。广泛的社会应用中的模拟工作流程，包括但不限于药物设计和气候变化研究。智能替代品的开发包括三个平行但相互交织的方法论、基础设施和领域评估主旨：1)主旨 I – 方法创新：该主旨在深度主动学习方面进行了根本性创新，在配备不确定性量化的贝叶斯环境中联合优化训练数据选择和神经架构，这使得智能代理能够支持训练模拟的智能主动选择。神经架构的动态调整；2) Thrust II – 基础设施创新）：该推力设计、实现和传播 RADICAL Optimal & Smart-Surrogate Explorer (ROSE)支持并行和自适应执行模拟和代理训练和选择任务的工具包。；3) 推力 III – 科学创新：该推力为智能代理在两个领域问题中的开发和评估奠定了基础：1) 具有奇异初始值的扩散方程的代理2) 个性化虚拟心脏模拟，建立在团队过去与既定领域合作者的合作基础上，这可以实现快速原型设计，同时为采用 Smart 的后续研究奠定基础。更广泛的复杂科学模拟的替代品。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。