Abstract:
Industrial AI is undergoing a fundamental transition from task-specific machine learning models toward general-purpose AI systems capable of learning, reasoning, and supporting autonomous decision-making across diverse engineering applications. However, industrial systems pose unique challenges arising from sparse labels, complex spatiotemporal dependencies, heterogeneous sensor networks, evolving operating conditions, and strict requirements on reliability, interpretability, and safety. Addressing these challenges requires AI systems that move beyond purely data-driven prediction by integrating physical knowledge, engineering structure, and autonomous reasoning.This talk presents recent advances toward scalable Industrial AI along four complementary directions. First, spatiotemporal graph neural networks (GNNs)exploit the topology and interactions of engineering systems to model spatial and temporal dependencies, enabling improved monitoring, forecasting, virtual sensing, and fault detection across interconnected assets. Second, physics-informed learning embeds physical inductive biases, such as governing equations, conservation laws, symmetries, and domain knowledge, into machine learning models, enabling more data-efficient, physically consistent, and robust learning with improved extrapolation beyond observed operating conditions. Third, tabular foundation models introduce a new paradigm based on in-context learning, enabling a single pretrained model to solve diverse industrial prediction tasks with little or no task-specific retraining. This opens new opportunities for scalable industrial AI, while also raising important questions about how structured engineering data, including temporal signals and system-level dependencies, should be represented for foundation-model-based inference. Finally, agentic AI extends predictive models toward intelligent engineering assistants by combining foundation models with graph representations, digital twins, external tools, and domain knowledge to perform autonomous root-cause analysis, evidence-based reasoning, and interactive diagnosis of industrial systems.Drawing on applications in intelligent maintenance, energy systems, and industrial process monitoring, the talk illustrates how these complementary paradigms collectively enable the next generation of scalable and trustworthy Industrial AI. By integrating graph-based representations, physics-informed learning, foundation models, and agentic reasoning, future AI systems will combine predictive intelligence with physical understanding and reasoning capabilities, paving the way toward intelligent engineering assistants that support diagnosis, root-cause analysis, and, ultimately, autonomous decision-making in complex engineering systems.

15:35 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room E 7

ETH Zürich

Risk Day

Arthur Gervais (University College London)

Would You Hire an AI as your Next Security Engineer? abstract

16:10 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room E 7

ETH Zürich

Risk Day

Rama Cont (University of Oxford)

Harnessing GenAI for Risk Management: Data-Driven Hedging with Generative Models abstract

17:10 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room E 7

ETH Zürich

Risk Day

Daniel Grosshans (Polykern)

Extreme Risk in High-Dimensional Portfolios abstract

17:45 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room E 7

address: Alessandra Naldi Windler, Departement Mathematik, HG G 51.4, ETH Zurich, Raemistrasse 101, CH-8092 Zurich
email: bulletin@math.ch | phone: +41 44 632 3684 | url: https://math.ch

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