SCience-INtegrated Predictive modeLing (SCINPL): a novel framework for scalable and interpretable predictive scientific modeling

科学集成预测建模（SCINPL）：用于可扩展和可解释的预测科学建模的新颖框架

• 批准号: 2210729
• 负责人: Simon Mak
• 金额: $ 20万
• 依托单位: Duke University
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210729&HistoricalAwards=false
• 关键词: SCience; INtegrated; Predictive; modeLing; SCINPL

Scientific modeling is at a critical and defining crossroads. With breakthroughs in experimental technology, high-quality data can now be obtained for complex scientific and engineering problems. However, the generation of such high-quality data entails large experimental and computational costs, resulting in limited data for scientific investigation. While predictive modeling provides some relief, recent work has revealed two key shortcomings with existing models: they often yield poor predictive performance when trained with limited data, and can violate established scientific principles, which may lead to erroneous and spurious scientific conclusions. This project will develop a novel SCience-INtegrated Predictive modeLing (SCINPL) framework which addresses these limitations. SCINPL paves the road for transformative scientific research, equipping practitioners with accurate, cost-efficient and interpretable predictive models for guiding scientific progress. This framework can catalyze closer collaborations between the scientific and data science communities, by demonstrating the practical advantages of science-driven statistical learning and data-driven scientific discovery. SCINPL provides a radical paradigm shift for scientific discovery in a broad range of fields, enabling scientists to push forward the frontiers of scientific knowledge and engineering via improved science-based data science tools.SCINPL features a suite of new probabilistic Bayesian models, which are capable of integrating a wide range of prior scientific domain knowledge as prior beliefs for predictive modeling. This integration of scientific knowledge with data-driven models not only provides improved predictive performance with reduced uncertainty, but also enables better interpretability and thus scientific discovery given limited training data. The first model, called the Boundary-constrained GP model, integrates known boundary information for the response surface within a Gaussian process (GP) framework. The second model, the Graphical Multi-fidelity GP model, embeds dependency information between scientific models for predictive modeling. The third model, the Gaussian Process Subspace regression model, integrates subspace information representing dominant physics for GP modeling. For each model, the investigators will (i) establish a solid theoretical foundation for predictive modeling, which demonstrates the improved predictive performance via the integration of scientific information, (ii) present a comprehensive methodological framework and efficient suite of algorithms for performing this desired integration of scientific principles within probabilistic modeling, and (iii) demonstrate the usefulness of such models for cost-efficient, interpretable and principled scientific discovery. Major emphasis is placed on demonstrating the effectiveness of SCINPL in tackling a broad range of complex and expensive scientific problems, including the design of 3D-printed aortic valves, the study of heavy-ion collisions, and the optimization of rocket engines for spaceflight.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.

科学建模正处于一个关键和决定性的十字路口。随着实验技术的突破，现在可以获得复杂科学和工程问题的高质量数据。然而，生成这种高质量的数据需要大量的实验和计算成本，导致用于科学调查的数据有限。虽然预测建模提供了一些缓解，但最近的工作揭示了现有模型的两个关键缺点：当使用有限的数据进行训练时，它们通常会产生较差的预测性能，并且可能违反既定的科学原则，这可能导致错误和虚假的科学结论。该项目将开发一种新的科学集成预测建模（SCINPL）框架，以解决这些限制。SCINPL为变革性科学研究铺平了道路，为从业者提供准确，具有成本效益和可解释的预测模型，以指导科学进步。该框架可以通过展示科学驱动的统计学习和数据驱动的科学发现的实际优势，促进科学界和数据科学界之间更紧密的合作。SCINPL为广泛领域的科学发现提供了根本性的范式转变，使科学家能够通过改进的基于科学的数据科学工具推进科学知识和工程的前沿。SCINPL具有一套新的概率贝叶斯模型，能够将广泛的先验科学领域知识整合为预测建模的先验信念。科学知识与数据驱动模型的这种整合不仅提高了预测性能，降低了不确定性，而且在有限的训练数据下实现了更好的可解释性，从而实现了科学发现。第一个模型，称为边界约束GP模型，集成了已知的边界信息的高斯过程（GP）框架内的响应面。第二个模型是图形多保真度GP模型，它嵌入了科学模型之间的依赖性信息，用于预测建模。第三个模型，高斯过程子空间回归模型，集成子空间信息代表主导物理GP建模。对于每个模型，研究人员将（i）为预测建模建立坚实的理论基础，通过整合科学信息来展示改进的预测性能，（ii）提出一个全面的方法框架和有效的算法套件，用于在概率建模中执行这种所需的科学原理整合，以及（iii）证明这种模型的实用性，可解释的和有原则的科学发现。主要重点是展示SCINPL在解决广泛的复杂和昂贵的科学问题方面的有效性，包括3D打印主动脉瓣的设计，重离子碰撞的研究，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

Adaptive design for Gaussian process regression under censoring

• DOI: 10.1214/21-aoas1512
• 发表时间: 2019-10
• 期刊: The Annals of Applied Statistics
• 作者: Jialei Chen;Simon Mak;V. R. Joseph;Chuck Zhang

A hierarchical expected improvement method for Bayesian optimization

• DOI: 10.1080/01621459.2023.2210803
• 发表时间: 2019-11
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Zhehui Chen;Simon Mak;C. F. J. Wu

Gaussian Process Subspace Prediction for Model Reduction

用于模型简化的高斯过程子空间预测

• DOI: 10.1137/21m1432739
• 发表时间: 2022
• 期刊: SIAM Journal on Scientific Computing
• 影响因子: 3.1
• 作者: Zhang, Ruda;Mak, Simon;Dunson, David
• 通讯作者: Dunson, David

Population Quasi-Monte Carlo

• DOI: 10.1080/10618600.2022.2034637
• 发表时间: 2020-12
• 期刊: Journal of Computational and Graphical Statistics
• 影响因子: 2.4
• 作者: Chaofan Huang;V. Roshan;Joseph H. Milton;Simon Mak

TSEC: A Framework for Online Experimentation under Experimental Constraints

TSEC：实验约束下的在线实验框架

• DOI: 10.1080/00401706.2022.2125443
• 发表时间: 2022
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Mak, Simon;Zhao, Yuanshuo;Hoang, Lavonne;Wu, C. F.
• 通讯作者: Wu, C. F.