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.

IN-DEEP是一个欧洲博士网络，由九名博士候选人（DC）和顶尖科学家组成，他们在应用数学，人工智能，高性能计算和工程应用方面具有互补的专业知识。其主要目标是为九个区议会提供高水平的培训，以及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偏微分方程（PDE）控制的逆问题。通过PDE将未知参数与实验测量值相连接的逆问题涵盖了医学应用-如癌症生长评估-民用基础设施的安全以及地热能生产等绿色地球物理应用。它们的应用价值是以人的生命和社会福祉来衡量的，这超出了任何可量化的金钱数额。这就是为什么为即将到来的向安全和强大的基于人工智能的技术过渡而为新一代专家配备高要求的技能势在必行。尽管在许多应用中取得了可喜的成果，但用于PDE的DL具有严重的局限性。最麻烦的是它缺乏坚实的理论背景和可解释性，这阻止了潜在用户将其集成到高风险应用程序中。IN-DEEP旨在消除这些限制，以释放DL算法在PDE中的全部潜力。我们将通过以下方式实现这一目标：（a）关注深度学习在具有巨大社会和/或工业价值的偏微分方程中的新兴应用，（B）设计注入数学的高级求解器以有效地解决这些问题，（c）从一开始就涉及能够监控、升级和利用这些知识的工业和技术代理。在此过程中，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，并在全球范围内传播成果。