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A canonical problem of axially strained flow in a duct of circular-sector cross-section, with fluid injection through the circular arc, is examined for a range of Reynolds numbers and sector angles. At small Reynolds numbers, the flow…

Fluid Dynamics · Physics 2025-08-19 Prabakaran Rajamanickam

Recent research has shed light on the role of coherent structures in forming layers when stably stratified turbulence is forced with horizontal shear (Lucas, Caulfield & Kerswell, J. Fluid Mech., vol. 832, 2017, pp. 409-437). Here we extend…

Fluid Dynamics · Physics 2019-05-01 Dan Lucas , C. P. Caulfield , Rich R. Kerswell

Causal structures give us a way to understand the origin of observed correlations. These were developed for classical scenarios, but quantum mechanical experiments necessitate their generalisation. Here we study causal structures in a broad…

Quantum Physics · Physics 2021-06-30 Mirjam Weilenmann , Roger Colbeck

Prandtl's secondary flows of the second kind generated by laterally-varying roughness are studied using the linearised Reynolds-Averaged Navier-Stokes approach proposed in Zampino et al (2022). The momentum equations are coupled to the…

Fluid Dynamics · Physics 2024-12-18 D. Lasagna , G. Zampino , B. Ganapathisubramani

When subjected to sufficiently strong velocity gradients, solutions of long, flexible polymers exhibit flow instabilities and chaotic motion, often referred to as elastic turbulence. Its mechanism differs from the familiar, inertia-driven…

Fluid Dynamics · Physics 2022-07-13 Alexander Morozov

We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions. In particular, we exploit the fact that…

Machine Learning · Statistics 2020-07-28 Ricardo Pio Monti , Ilyes Khemakhem , Aapo Hyvarinen

When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables. However, traditional Granger causal inference has limited utility in…

Machine Learning · Computer Science 2022-10-20 Rohit Singh , Alexander P. Wu , Bonnie Berger

We develop a criterion to certify whether causal effects are identifiable in linear structural equation models with latent variables. Linear structural equation models correspond to directed graphs whose nodes represent the random variables…

Statistics Theory · Mathematics 2025-07-25 Nils Sturma , Mathias Drton

We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, $\sigma$, of the variables such that each observed variable $Y_v$ is a linear function of a variable specific…

Methodology · Statistics 2019-06-28 Y. Samuel Wang , Mathias Drton

Deformable microchannels emulate a key characteristic of soft biological systems and flexible engineering devices: the flow-induced deformation of the conduit due to slow viscous flow within. Elucidating the two-way coupling between…

Fluid Dynamics · Physics 2026-01-14 Anxu Huang , Shrihari D. Pande , Jie Feng , Ivan C. Christov

The nonequilibrium steady state of a granular fluid, driven by a random external force, is demonstrated to exhibit long range correlations, which behave as $\sim 1/r$ in three and $\sim \ln(L/r)$ in two dimensions. We calculate the…

Statistical Mechanics · Physics 2009-10-31 T. P. C. van Noije , M. H. Ernst , E. Trizac , I. Pagonabarraga

Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent…

Methodology · Statistics 2026-03-05 Daniele Tramontano , Mathias Drton , Jalal Etesami

Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical…

Methodology · Statistics 2023-06-27 Raj Agrawal , Chandler Squires , Neha Prasad , Caroline Uhler

Intermittent high-amplitude structures emerge in a damped and driven discrete nonlinear Schroedinger equation whose solutions transport both energy and particles from sources to sinks. These coherent structures are necessary for any…

Chaotic Dynamics · Physics 2007-05-23 Benno Rumpf Alan C. Newell

The inference of causal relationships among observed variables is a pivotal, longstanding problem in the scientific community. An intuitive method for quantifying these causal links involves examining the response of one variable to…

Statistical Mechanics · Physics 2025-03-27 Gabriele Di Antonio , Gianni Valerio Vinci

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although…

Statistical Finance · Quantitative Finance 2023-12-29 Haochun Ma , Davide Prosperino , Alexander Haluszczynski , Christoph Räth

We address the problem to estimate a dynamic network whose edges describe Granger causality relations and whose topology has a Kronecker structure. Such a structure arises in many real networks and allows to understand the organization of…

Optimization and Control · Mathematics 2022-06-13 Mattia Zorzi

In the framework of the theory of partially fluidized granular flows we study the formation of longitudinal structures observed experimentally by Forterre and Pouliquen in a flow down a rough inclined plane. We show that the formation of…

Materials Science · Physics 2016-08-31 Igor S. Aranson , Lev S. Tsimring

Conduits generated by the buoyant dynamics between two miscible, Stokes fluids with high viscosity contrast exhibit rich nonlinear wave dynamics. However, little is known about the fundamental wave dispersion properties of the medium. In…

Fluid Dynamics · Physics 2023-01-18 Yifeng Mao , Mark A. Hoefer

Much of the past work in network analysis has focused on analyzing discrete graphs, where binary edges represent the "presence" or "absence" of a relationship. Since traditional network measures (e.g., betweenness centrality) utilize a…

Social and Information Networks · Computer Science 2011-04-05 Joseph J. Pfeiffer , Jennifer Neville