Industrial and Applied Mathematics Seminar

The integration of AI into computational fluid dynamics (CFD) represents a transformative frontier for engineering, yet realizing this potential requires navigating the complexities inherent to fluid mechanics. Bridging the methodological gap between deep learning and traditional CFD simulation, this talk presents work (outlined in the recent preprint: Fluids Intelligence: A forward look on AI foundation models in computational fluid dynamics) to produce a novel scaling law tailored specifically for a fluids foundation model. We explore the theoretical and practical opportunities, analyzing the critical inflection points where model training compute begins to eclipse the high costs of traditional data generation. We conclude by discussing the technical challenges and opportunities the fluids and machine learning communities must collaboratively address to operationalize autonomous computational engineering.

Insects use flight as far more than a means of getting from A to B. Flight creates an aeiral theatre for interaction, whether between species or among members of the same species. For example, a male dragonfly must hunt for food, fend off rival males, and pursue evasive females in order to reproduce, tasks that all revolve around chasing fast-moving targets. Despite the remarkable diversity of insect species and their aerial behaviours, common patterns emerge in how they exploit speed and manoeuvrability to achieve these goals. Simple geometric guidance laws can describe these flight trajectories with surprising accuracy, revealing shared strategies that underpin insect aerial combat.

For more than a hundred years, scientists have carefully analysed the apparently random fluctuations in Brownian trajectories to learn about soft systems. In a more general sense, however, the information hidden within experimental fluctuations is typically underexploited, due to challenges in unambiguously linking fluctuation signatures to underlying physical mechanisms. In this talk, I will discuss our recent work developing new approaches to interpreting fluctuations in experimental data from a variety of soft systems, and thereby turn ‘noise’ into signal. In particular, I will share some recent results taking a fresh look at fluctuations in equilibrium colloidal monolayers. Here, we have combined experiment, simulation and theory to explore how simply counting colloids can reveal details of self and collective dynamics in interacting systems [1,2,3]. I will then discuss ongoing work to extend this understanding to confined driven systems [4], with the long-term goal of elucidating characteristic fluctuations in our synthetic nanopore experiments [5].

[1] E. K. R. Mackay, B. Sprinkle, S. Marbach, A. L. Thorneywork, Phys. Rev X. (2024)

[2] A. Carter, ALT et al., Soft Matter, 21, 3991, (2025)

[3] E. K. R. Mackay, ALT et al., arXiv:2512.17476, (2025)

[4] S. F. Knowles, E. K. R. Mackay, A. L. Thorneywork, J. Chem. Phys., (2024)

[5] S. F. Knowles, A. L. Thorneywork et al., Phys. Rev. Lett, 127, 137801, (2021)

Core-annular flows are often proposed to reduce frictional losses in industrial pipeline transport processes. Traditionally, a low-viscosity lubricating film is placed around a more viscous core to reduce the drag on the core. However, maintaining stable pipelining, where the core and the lubricant remain separated has proved challenging.
In this talk we present an alternative approach using three-layer, horizontal core-annular pipe flow, in which two fluids are separated by a deformable elastic solid. In the experiments, an elastic solid created by an in-situ chemical reaction maintains the separation of the core and annular fluids. Corrugations of the elastic interface are observed and stable pipelining, where the elastic shell created separating the two fluids remains intact, is successfully demonstrated even when the core fluid is buoyant. We also develop a theoretical model combining lubrication theory for the fluids with standard shell theory for the elastic solid, to predict the buckling states resulting from radial compression of the shell.
The self-sculpting of the shell by buckling cannot by itself generate hydrodynamic lift owing to symmetry in the direction of flow. Instead, we demonstrate that hydrodynamic lift can be achieved by other elastohydrodynamic effects, when that symmetry becomes broken during the bending of the shell.

A central challenge in applied mathematics is to extract predictive structure from data generated by complex dynamical systems. Koopman operator methods provide a principled framework for this task by embedding nonlinear dynamics into a linear operator acting on observables, reducing analysis and forecasting to questions about spectral approximation.

In this talk, I will present recent results on the analysis of data-driven Koopman methods, with an emphasis on when spectral quantities can be reliably approximated from finite data. I will describe a general framework that connects operator-theoretic properties of the Koopman operator with the behaviour of practical algorithms, clarifying phenomena such as spectral pollution and the role of continuous spectra. I will also discuss fundamental limitations: there exist classes of dynamical systems for which finite data cannot recover meaningful spectral information, placing intrinsic constraints on what Koopman-based approaches can achieve. Building on this, I will show how spectral approximation errors translate into quantitative bounds for forecasting, capturing how approximation and statistical errors propagate over time and ultimately limit long-term prediction. These results have implications for applications including fluid dynamics, molecular systems, and geophysical flows. I will conclude by highlighting open problems at the intersection of operator theory, numerical analysis, and scientific machine learning.