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.

小企业创新研究 (SBIR) 第一阶段项目的更广泛影响将来自于在工程和医疗保健等广泛领域开发新的、更高效的设计时节省大量成本和时间。如果成功，该项目将通过使用先进的数字模拟来表示系统的物理组件，从而使从汽车到航空航天组件和药品的所有模拟运行速度提高 100 倍。 迄今为止，软件不兼容性限制了此类建模的发展。该项目将通过先进的计算和编译技术解决这个问题，从而证明一种新型设计过程的可行性，并显着降低成本。 这个小企业创新研究第一阶段项目将展示在模块化系统中使用神经组件的可行性。 我们将结合代理模型优化和神经常微分方程的成功经验，以实现基于组件的微分代数方程模型，并通过潜在 diffeq 实现自动模型降阶。我们的想法是使用我们在差分编程和学习软件方面的最新进展，将复杂模型构建为模块化预设计模拟组件的集合，以允许自动训练神经模型降阶，从而加速大型非因果模型的解决方案。两种机器学习方法在加速传统机械建模工作流程方面具有广阔的前景：代理优化和神经微分方程。 在这个项目中，我们将把这些组件集成到一个原型系统中。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。