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.

在广泛的科学领域中，高保真计算机模拟基于发现的基础。但是，他们的计算成本限制了它们的全部潜力。在近似深度神经网络近似科学模拟方面的努力越来越大，以通过数量级加速模拟工作流程。但是，当前的实践在很大程度上依赖于固定网络架构和离线模拟数据 - - 由经验定义，而不是通过定量指标进行优化。这导致了经验，主观和实验室实践，但结果却是最佳的结果。这项研究通过用于开发智能替代物的新概念，数学和基础设施框架来解决上述关键差距。作为一个域形不足的框架，智能替代人将及时支持对科学模拟的替代建模的日益增加但尚未到来的需求。该项目中创建的原型替代物还将直接在每个涉及的域中进行长期的后续研究。这项合作研究在人工智能，高性能计算和科学模拟的交集中提供了多学科培训，这有助于准备下一代研究人员熟练于跨学科思维和技能。它计划主动从代表性不足的群体中招募学生，并为智能代理人开展动手实践研讨会，以传播向更广泛的学生团体。最后，将Rose作为开源工具包的传播将影响HPC模拟工作流程在广泛的社交应用中，包括但不限于药物设计和对气候变化的研究。智能代理的发展包括三种平行但交织的方法论，基础构造和范围的挑战，始终如一地进行了挑战，这是一种挑战，这是挑战的基础：1）促进了基础的创新：1）构成基础的创新：1）构成了基础的创新：1）。在配备不确定性定量的贝叶斯环境中，训练数据选择和神经体系结构。这使智能替代物能够支持智能主动选择训练模拟以及对神经体系结构的动态调整； 2）推力II - 基础架构创新）：这种推力设计，实施和传播了激进的最佳和智能传播探索器（Rose）工具包，以支持同时和适应性执行模拟和替代培训和选择任务。 3）推力III - 科学创新：这推动了两个领域问题中智能替代物的发展和评估：替代物的替代物的1）具有奇异初始条件的扩散方程和2）个性化的虚拟心脏模拟，建立在团队过去与已建立的领域协作者的过去合作的基础上。这允许快速原型制作，同时为连续的后续研究奠定了基础，以在更大的复杂科学模拟中采用智能替代物。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和更广泛的影响来审查标准，认为通过评估的评估被认为是宝贵的支持。