SBIR Phase I: Neural Component Architecture to Accelerate Modeling & Simulation

SBIR 第一阶段：加速建模的神经组件架构

• 批准号: 1938400
• 负责人: Keno Fischer
• 金额: $ 22.47万
• 依托单位: Julia Computing Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938400&HistoricalAwards=false
• 关键词: SBIR; Phase; Neural; Component; Architecture

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from enabling significant cost and time savings in developing new, more efficient designs in broad fields such as engineering and healthcare. If successful, the project will enable simulations of everything from automobiles to aerospace components and pharmaceuticals to run up to 100 times faster by representing a physical component of a system with an advanced digital analogue. To date, software incompatibilities have limited the development of this kind of modeling. This project will solve this problem through advanced computational and compiler techniques, and thereby demonstrate the feasibility of a new kind of design process with significant cost reductions. This Small Business Innovation Research Phase I project will demonstrate the feasibility of using neural components in a modular system. We will combine the successes of surrogate model optimization and neural ODEs to allow for component-based differential-algebraic equation models with automated model order reduction through a latent diffeq. The idea is to build complex models as an assembly of modular pre-designed simulation components using our recent advances in differential programming and learning software to allow for automated training of neural model order reduction for accelerating the solution of large acausal models. Two machine learning methods have promising prospects for accelerating traditional mechanistic modeling workflows: surrogate optimization and neural differential equations. In this project, we will integrate these components into a prototype system.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响将是由于在工程和医疗保健等广泛领域开发新的，更高效的设计方面的成本和时间节省。如果成功，该项目将通过代表具有先进数字模拟的系统的物理组件，对从汽车到航空航天组件和药品的所有内容进行模拟。 迄今为止，软件不兼容限制了这种建模的开发。该项目将通过高级计算和编译器技术解决此问题，从而证明了一种新型设计过程的可行性，并大大降低了成本。 这个小型企业创新研究阶段项目将证明在模块化系统中使用神经组件的可行性。 我们将结合替代模型优化和神经ODE的成功，以使基于组件的微分 - 代数方程模型与通过潜在DIFFEQ的自动模型订单降低。这个想法是使用我们在差异编程和学习软件中的最新进步来构建复杂的模型作为模块化预设计的模拟组件组装，以允许自动培训神经模型订单降低，以加速大型Acausal模型的解决方案。两种机器学习方法具有加速传统机械建模工作流程的有希望的前景：替代优化和神经微分方程。 在该项目中，我们将将这些组件集成到原型系统中。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响审查标准的评估值得支持的。