Real-time inversion using self-explainable deep learning driven by expert knowledge

使用由专家知识驱动的可自我解释的深度学习进行实时反演

• 批准号: EP/Z000653/1
• 负责人: Kristoffer Van Der Zee
• 金额: $ 33.22万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ000653%2F1
• 关键词: Real; time; inversion; using; self

IN-DEEP is a European Doctoral Network composed of nine doctoral candidates (DCs) and top scientists with complementary areas of expertise in applied mathematics, artificial intelligence, high-performance computing, and engineering applications. Its main goal is to provide high-level training to the nine DCs in designing, implementing, and using explainable knowledge-driven Deep Learning (DL) algorithms for rapidly and accurately solving inverse problems governed by partial differential equations (PDEs).Inverse problems in which the unknown parameters are connected to experimental measurements through PDEs cover from medical applications - like cancer growth assessment - to the safety of civil infrastructures, and green geophysical applications such as geothermal energy production. Their application value is measured in human lives and society's well-being, which goes beyond any quantifiable amount of money. This is why equipping a new generation of specialists with highly-demanded skills for the upcoming transition toward safe and robust AI-based technologies is imperative.Despite the promising results in many applications, DL for PDEs has severe limitations. The most troublesome is its lack of a solid theoretical background and explainability, which prevents potential users from integrating them into high-risk applications. IN-DEEP aims to remove these constraints to unleash the full potential of DL algorithms for PDEs. We will achieve this by: (a) focusing on emerging applications of DL for PDEs with immense societal and/or industrial value, (b) designing mathematics-infused advanced solvers to address them efficiently, and (c) involving, from the beginning, industrial and technological agents which can monitor, upscale, and exploit this knowledge. On the way, we shall establish the foundations of a better knowledge exchange ecosystem amongst the main academic and industrial actors within Europe, disseminating the results worldwide.

深入的是欧洲博士网络，由九个博士候选人（DC）和高级科学家组成，具有互补的应用数学，人工智能，高性能计算和工程应用方面的专业知识。它的主要目标是在设计，实施，实施，并使用可解释的知识驱动的深度学习（DL）算法来快速，准确地求解由部分微分方程（PDES）控制的逆问题（PDES）。绿色的地球物理应用，例如地热能生产。它们的应用价值是在人类生活和社会的福祉中衡量的，这超出了任何可量化的资金。这就是为什么要为新一代专家提供高度要求的技能，以实现即将到来的过渡，以实现安全且强大的基于AI的技术。最麻烦的是它缺乏可靠的理论背景和解释性，这阻止了潜在用户将其集成到高风险应用程序中。深入的目的是消除这些约束，以释放PDE的DL算法的全部潜力。我们将通过以下方式实现：（a）专注于DL在具有巨大的社会和/或工业价值的PDE上的新兴应用，（b）设计具有数学注入的高级求解器以有效地解决它们，并从一开始，从一开始，可以监测这些知识的工业和技术代理。在途中，我们将在欧洲的主要学术和工业参与者之间建立更好的知识交流生态系统的基础，从而在全球范围内传播结果。