Strategic Package: Centre for Predictive Modelling in Science and Engineering

战略计划：科学与工程预测建模中心

• 批准号: EP/L027682/1
• 负责人: Nigel Stocks
• 金额: $ 98.05万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL027682%2F1
• 关键词: Strategic; Package; Centre; Predictive; Modelling

Predictive modelling and uncertainty quantification are more than just another research direction relevant to science and engineering. They constitute a different way of thinking that impacts practically all aspects of scientific and engineering analysis and design. Rather than deriving deterministic answers to complex problems, distributions (error-bars) are obtained that account for our incomplete and often inaccurate information about the problems of interest. Modelling of uncertain physical and geometric properties in the large scales, intrinsic randomness in the small scales or incorporation of stochastic closures necessitate the use of stochastic simulations in many multiscale problems, heavily taxing computational resources. The emerging exascale computers will allow stochastic simulations of realistic systems but analysing petabyte data bases with existing methods can be too inefficient, and full-scale models cannot be used in optimisation, design or real-time control. To this end, the University of Warwick proposes to establish a new centre focused on Predictive Modelling and uncertainty quantification with the mission to develop computational, mathematical and statistical methodologies for uncertainty quantification (UQ) and predictive modelling applied to a broad range of applications. The new centre will emphasises a dynamic integration of several fundamental research areas including the following:1) Formulation, analysis and numerical solution of stochastic multiscale/multiphysics systems exploring synergies between mathematical, statistical and machine learning approaches.2) Data-driven development of certified stochastic reduced-order models. 3) Development of a probabilistic approach to systems synthesis, design, optimisation and control under uncertainty. This proposed work will develop methods for quantifying uncertainty in multiscale simulations driven by experimental data collected at different scales. In order to account for the high-dimensional nature of the input uncertainties and resulting stochastic outputs, it is proposed to develop reduced-order surrogate models both for the input uncertainties and for the output of multiscale systems. Topics (1)-(3) are unifying themes that are common to many disciplines. Bringing together existent strengths within the University of Warwick to address these problems would allow simultaneously to impact multiple applications and move forward the problem of resolving bottlenecks in uncertainty quantification (e.g. high-dimensionality, limited data, long term time integration, rare events).

预测建模和不确定性量化不仅仅是与科学和工程相关的另一个研究方向。它们构成了一种不同的思维方式，几乎影响了科学和工程分析和设计的所有方面。而不是得出确定性的答案复杂的问题，分布（误差条），占我们的不完整，往往是不准确的信息有关的问题。在大尺度的不确定的物理和几何特性，内在的随机性在小尺度或纳入随机封闭的建模需要使用随机模拟在许多多尺度问题，沉重的计算资源。新兴的亿亿级计算机将允许对现实系统进行随机模拟，但用现有方法分析PB级数据库可能效率太低，并且全尺寸模型不能用于优化，设计或实时控制。为此，沃里克大学提议建立一个新的中心，专注于预测建模和不确定性量化，其使命是开发用于广泛应用的不确定性量化（UQ）和预测建模的计算，数学和统计方法。新中心将强调几个基础研究领域的动态整合，包括：1）随机多尺度/多物理系统的制定，分析和数值解，探索数学，统计和机器学习方法之间的协同作用。3)发展不确定性下系统综合、设计、优化和控制的概率方法。这项拟议的工作将开发方法，用于量化在不同尺度收集的实验数据驱动的多尺度模拟的不确定性。为了考虑输入不确定性和随机输出的高维性质，建议为输入不确定性和多尺度系统的输出建立降阶代理模型。主题（1）-（3）是许多学科共同的统一主题。将沃里克大学现有的优势结合起来解决这些问题，将允许同时影响多个应用程序，并向前推进解决不确定性量化中的瓶颈问题（例如，高维，有限的数据，长期时间积分，罕见事件）。

项目成果

Machine learning unifies the modeling of materials and molecules.

• DOI: 10.1126/sciadv.1701816
• 发表时间: 2017-12
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
• 通讯作者: Ceriotti M

Atomistic aspects of fracture

• DOI: 10.1007/s10704-015-9988-2
• 发表时间: 2015-02-01
• 期刊: INTERNATIONAL JOURNAL OF FRACTURE
• 影响因子: 2.5
• 作者: Bitzek, Erik;Kermode, James R.;Gumbsch, Peter
• 通讯作者: Gumbsch, Peter

Exploiting Machine Learning in Multiscale Modelling of Materials

• DOI: 10.1007/s40033-022-00424-z
• 发表时间: 2022-11
• 期刊: Journal of The Institution of Engineers (India): Series D
• 作者: G. Anand;Swarnava Ghosh;Liwei Zhang;Angesh Anupam;Colin L. Freeman;C. Ortner;M. Eisenbach;J. Kermode

Machine Learning a General-Purpose Interatomic Potential for Silicon

• DOI: 10.1103/physrevx.8.041048
• 发表时间: 2018-12-14
• 期刊: PHYSICAL REVIEW X
• 影响因子: 12.5
• 作者: Bartok, Albert P.;Kermode, James;Csanyi, Gabor
• 通讯作者: Csanyi, Gabor

Classical interaction potentials for diverse materials from ab initio data: a review of potfit

• DOI: 10.1088/0965-0393/23/7/074002
• 发表时间: 2014-11
• 期刊: Modelling and Simulation in Materials Science and Engineering
• 影响因子: 1.8
• 作者: P. Brommer;A. Kiselev;D. Schopf;P. Beck;J. Roth;H. Trebin