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

高保真的计算机模拟为广泛的科学领域的发现奠定了基础。然而，它们的计算成本限制了它们的全部潜力。在用深度神经网络近似科学模拟方面，人们已经做出了越来越多的努力，以将模拟工作流程加速数量级。然而，当前的实践在很大程度上依赖于固定的网络体系结构和离线模拟数据--由经验预先定义，而不是通过量化指标进行优化。这导致了一种经验性的、主观的和费力的实践，但结果并不理想。这项研究通过开发智能代理的新概念、数学和基础设施框架解决了上述关键差距。作为一个领域不可知的框架，智能代理将为不断增长但尚未满足的科学模拟代理建模需求提供及时的支持。在这个项目中创建的原型代理还将直接支持在每个涉及的领域进行长期后续研究。这项合作研究在多个领域提供人工智能、高性能计算和科学模拟交叉领域的多学科培训，帮助培养擅长跨学科思维和技能的下一代研究人员。它计划积极地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的动手研讨会，以便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模拟工作流在广泛的社会应用中的应用，包括但不限于药物设计和气候变化研究。智能代理的开发包括三个并行但相互交织的方法学、基础设施和领域评估推力：1)推力I-方法学创新：这一推力发展了深度主动学习的基本创新，以在配备了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体系结构。这使得智能代理能够支持训练模拟的智能主动选择以及神经结构的动态调整；2)推力II-基础设施创新)：这一推力设计、实施和传播基本的最优和智能代理探索者(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)推力III-科学创新：这一推力基于两个领域问题的智能代理的开发和评估：1)具有奇异初始条件的扩散方程的代理；2)个性化虚拟心脏模拟，基于团队过去与已建立的领域合作者的工作。这可以实现快速原型制作，同时为后续研究在更大范围的复杂科学模拟中采用智能代理奠定基础。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.