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.

高保真度的计算机模拟支持在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。人们越来越多地努力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，目前的实践在很大程度上依赖于固定的网络架构和离线模拟数据-通过经验预先定义，而不是通过定量指标进行优化。这导致了一种经验的、主观的、费力的实践，但结果并不理想。这项研究解决了上述关键的差距，一个新的概念，数学和基础设施框架，开发智能代理。作为一个与领域无关的框架，Smart Surrogates将为日益增长但尚未得到满足的科学模拟代理建模需求提供及时的支持。该项目中创建的原型替代品也将直接支持每个相关领域的长期后续研究。这项合作研究在人工智能，高性能计算和科学模拟的交叉点提供多学科培训，帮助培养擅长跨学科思维和技能的下一代研究人员。它计划积极从代表性不足的群体中招收学生，并开发一个关于智能代理人的实践讲习班，以向更广泛的学生群体传播。最后，ROSE作为一个开源工具包的传播将影响HPC模拟工作流程在广泛的社会应用，包括但不限于药物设计和气候变化的研究。智能代理的发展包括三个平行但交织的方法，基础设施和领域评估的推力：1）推力I -方法创新：这一推动力发展了深度主动学习的基本创新，以在配备不确定性量化的贝叶斯设置中联合优化训练数据选择和神经架构。这使得智能代理支持训练模拟的智能主动选择，沿着神经结构的动态调整; 2）Thrust II -基础设施创新）：这一推进设计、实现和推广了RADICAL Optimal Smart-Surrogate Explorer（ROSE）工具包，以支持模拟和代理训练和选择任务的并发和自适应执行。3)推进三-科学创新：这一推进为智能替代物在两个领域问题中的开发和评估奠定了基础：1）具有奇异初始条件的扩散方程的替代物和2）个性化的虚拟心脏模拟，建立在团队过去与既定领域合作者的工作基础上。该奖项反映了NSF的法定使命，并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。