SLAI Seminar 34th Session will be discussing the topic on "Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective ",  from 10:00am-11:30am, April 21 (Tuesday) at Room B401, online participation is welcome.

 (Tencent Meeting ID: 304-729-930)

报告主题：以人工智能赋能自然智能：数学家的视角

时间：2026年4月21日（周二）上午10:00-11:30

地点: 深圳河套学院B401教室

线上参与：腾讯会议号304-729-930

主讲嘉宾：Alfio Quarteroni教授

主持人：许进超教授

讲者简介About the Speaker:

Alfio Quarteroni 是米兰理工大学和洛桑联邦理工学院的荣休教授。他是米兰理工大学MOX（建模与科学计算实验室）的创始人。夸尔特罗尼是多个著名学院的院士，包括意大利林琴国家科学院（由伽利略创立，是欧洲最古老的科学学院）、欧洲人文与自然科学院、欧洲科学院、里斯本科学院、瑞士工程与技术科学院以及意大利工程与技术学院。他撰写过25本被翻译成多种语言的著作，并发表了超过450篇研究论文。他曾担任最后一届菲尔兹奖评选委员会的成员。

Quarteroni教授曾荣获众多奖项，包括美国国家航空航天局（NASA）团体成就奖（1992年）、2011年比萨高等师范学院的伽利略讲席、2015年国际伽利略·伽利雷科学奖、2022年ECCOMAS欧拉奖章、2023年ICIAM拉格朗日奖、2024年布莱兹·帕斯卡数学奖、2024年ECCOMAS里茨-伽辽金奖章以及2025年SIAM拉尔夫·克莱曼奖。

Quarteroni教授研究涵盖医学、地震地球物理学、环境科学、航空学和石油工业等多个应用领域。他曾为赢得2003年和2007年美洲杯帆船赛的瑞士阿灵基帆船的设计提供数学建模支持，并为全球首架太阳能飞机“阳光动力”号的初步数学项目做出贡献。他还开发了首个完整的人类心脏数学模型。根据谷歌学术的数据，Quarteroni教授是被引用次数最高的意大利数学家。

Alfio Quarteroni is an Emeritus Professor at Politecnico di Milano and at EPFL, Lausanne. He is the founder of MOX (the laboratory of Modeling and Scientific Computing) at Politecnico di Milano. Quarteroni is a member of several prestigious academies, including the Italian Accademia Nazionale dei Lincei (the oldest scientific Academy in Europe founded by Galileo Galilei), the European Academy of Sciences, the Academy of Europe, the Lisbon Academy of Sciences, the Swiss Academy of Engineering and Technology, and the Italian Academy of Engineering and Technology. He has authored 25 books translated into several laanguages and more than 450 research papers. He has been a member of the last Fields Medal Committee. Quarteroni has been honored with numerous awards, including the NASA – Group Achievement Awards (1992), the Galilean Chair from Scuola Normale Superiore in 2011, the International Galileo Galilei Prize for Sciences in 2015, the ECCOMAS Euler Medal in 2022, the ICIAM Lagrange Prize in 2023, the Blaise Pascal Prize for Mathematics in 2024, the ECCOMAS Ritz-Galerkin medal in 2024, the SIAM Ralph Kleinman Prize in 2025. His research spans applications in medicine, earthquake geophysics, environmental science, aeronautics, and the oil industry. He led the mathematical modeling for the design of Alinghi, the Swiss yacht that won the America’s Cup in 2003 and 2007, and for the preliminary mathematical project of Solar Impulse, the first solar-powered aircraft. He also developed the first comprehensive mathematical model of the human heart.According to Google Scholar, he is the most highly cited Italian mathematician.

报告摘要：

过去的几十年中，基于物理规律的计算模型，已深刻改变了我们理解和预测自然世界中复杂现象的能力。这些模型为科学见解与技术创新的蓬勃发展提供严谨的框架，体现了所谓的“自然智能”：这是数个世纪以来科学推理、数学结构和物理理解的累积成果。

本次报告首先将通过若干实例，阐述这一范式如何塑造现代计算科学。报告将讨论地震工程中的大规模模拟、利用数学模型提升运动表现的方法，以及一个涵盖完整心脏功能的综合数学模型。报告的第二部分将转向人工智能与机器学习，它们代表了一种截然不同的范式，主要由数据驱动，而非物理原理。我们将审视纯数据驱动方法的优势，重点介绍其取得的显著成功，同时也会讨论它们的局限性。

这些思考自然引出了新兴的“科学机器学习”领域。在这一领域中，基于物理的建模与数据驱动的方法不再被视为相互竞争的哲学，而是互为补充的智能来源。科学机器学习提供的新一代计算模型，兼具更高的准确性和可靠性。

从以上视角，“自然智能”与人工智能的融合，不仅是一项技术发展，更是一种观念上的转变：它提供了一个独特的机会，将科学的解释力与数据驱动学习的适应性结合起来。本次报告将阐明，这种融合代表了计算科学未来最有希望的方向之一，为理解复杂系统、将数学转化为惠及社会的实际成果开辟了新的路径。

Abstract:

Over the past decades, computational models grounded in physical laws have profoundly transformed our ability to understand and predict complex phenomena in the natural world. a These models, by providing rigorous framework in which scientific insight and technological innovation can flourish, embody what may be called natural intelligence: the cumulative result of centuries of scientific reasoning, mathematical structure, and physical understanding.This lecture begins by illustrating how this paradigm has shaped modern computational science through selected examples. We will discuss large-scale simulations in earthquake engineering, the use of mathematical modeling to enhance sports performances; and a comprehensive mathematical model of the complete cardiac function. The second part of the lecture turns to artificial intelligence and machine learning, which represent a fundamentally different paradigm, driven primarily by data rather than by physical principles. We will review the strengths of purely data-driven approaches, highlighting some of their remarkable success, while also discussing their limitations.These considerations naturally lead to the emerging field of Scientific Machine Learning, where physics-based modeling and data-driven methods are no longer seen as competing philosophies, but as complementary sources of intelligence. Scientific Machine Learning offers a new generation of computational models that are more accurate and trustworthy.In this perspective, the fusion of “natural intelligence” and artificial intelligence is not merely a technical development, but a conceptual shift: a unique opportunity to combine the explanatory power of science with the adaptive strength of data-driven learning. The lecture will argue that this synthesis represents one of the most promising directions for the future of computational science, opening new avenues for understanding complex systems and for translating mathematics into tangible benefits for society.