TBA 

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)

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.