arXiv subjects

Eleni Katifori

Publications and source records attributed to Eleni Katifori.

At least 19 recordsLinked to original sources

arXiv2026

Design principles for energy dissipation in viscoelastic network metamaterials

Mechanical energy dissipation in networked materials is relevant for applications from vibration isolation to impact protection, yet identifying optimal dissipative architectures in large disordered truss networks is computationally prohibitive with conventional finite element methods. We develop an efficient graph Laplacian-based spectral framework for viscoelastic truss networks, in which the full continuum dynamics of each rod are retained exactly and the problem size scales with the number of joints rather than element-level discretization points. Using this framework, we investigate how redistributing cross-sectional areas within a network (without changing material composition) controls energy dissipation. We find that random redistribution typically reduces dissipation relative to a uniform baseline, while gradient-based optimization yields nontrivial architectures whose form is governed by the intrinsic attenuation length of the base material. Focusing on driving frequencies near a global resonant mode of the network, we show that the optimal mass distribution decays from the source (driven joint) with the attenuation length scale, and at small attenuation lengths the optimal architecture is independent of the boundary conditions. These results motivate future studies of dissipation length scale based design principles on more complex disordered architectures and provide an efficient computational framework for exploring such structures at scale.

cond-mat.soft↗

arXiv2026

Length-scale selection in adaptive transport networks

Adaptive transport networks in biological and physical systems exhibit hierarchical organization, characteristic channel spacing, and robust scaling relations. Existing adaptive network models, formulated on a lattice, successfully reproduce many observed topologies and conduit scaling laws; however, the mechanism that selects network density and spatial spacing remains unclear. We address this in a continuum formulation where conductivity evolves as a tensor field coupled to pressure-driven flow. Linearizing about a homogeneous conducting state, we identify a finite-wavelength instability with a $-1/4$ preferred wavelength scaling in the control parameter. Simulations of the full equations confirm the analytical predictions and demonstrate the formation of anisotropic conducting structures above threshold. These results establish a scale-selection principle for adaptive transport network formation which arises from a pattern-forming instability rather than solely from relaxation within a nonconvex energy landscape. The instability mechanism places adaptive transport systems within a broader class of nonequilibrium pattern-forming media in which constitutive transport feedback generates spatial organization. Beyond reproducing hierarchical scaling laws, the theory additionally predicts the intrinsic density of transport networks and the spatial scale of resource delivery.

nlin.AO↗

arXiv2026

Unidirectional flow from continuous broken symmetries

Locally broken symmetries are used across fields to transport matter, particles and information in preferential directions. Beyond local mechanisms, spatially distributed nonlinearities in crystalline media have enabled non-reciprocal transport, a rectification mechanism that operates continuously across scales and frequencies. Here, we show that this concept applies beyond condensed matter, to fluid transport in living organisms and artificial systems. We take the example of the lymphatic vascular system, which transports interstitial fluid in mammals, and demonstrate that distributed leaflets act as continuous broken symmetries. We build an artificial model of a collecting lymphatic and investigate the naturally richer dynamics of unidirectional transport that arises from spatiotemporal excitations. We observe robust and scalable transport for any waveshape and external pressure gradients. We show experimentally and theoretically that the contraction wavelength, directionality, and pulsatility control the flow rate. In particular, we counterintuitively find waveshapes that maximize transport when propagating against the direction of the flow. Overall, our findings advance the understanding of unidirectional fluid transport in living systems and beyond, and reveal how coupling nonlinearities with spatiotemporal excitations can tune such transport across fields.

cond-mat.soft↗

arXiv2025

Hierarchical Loop Stabilization in Periodically Driven Elastic Networks

Network remodeling, or adaptation, in the presence of periodically driven forcings has hereto remained largely unexplored, despite the fact that a broad class of biological transport networks, e.g. animal vasculature, depends on periodic driving (pulsatility of the heart) to maintain flow. Short-term pulsatile dynamics of compliant vessels affects the long-term structures of adapting networks; however, what the correct adaptation rule is for pulsatile flows still remains an open question. Here we propose a new adaptation rule for periodically driven complex elastic networks that accounts for the effect of short-term pulsatile dynamics on the remodeling signal at long time-scales. Using this rule to adapt hierarchical elastic networks with multiple levels of looping, we show that very different network architectures are possible at steady-state depending on the driving frequency of the pulsatile source and the geometric asymmetry of the paths between the externally driven nodes of the network. Specifically resonant frequencies are shown to prioritize the stabilization of fully looped structures or higher level loops proximal to the source, whereas anti-resonant frequencies predominantly stabilize loop-less structures or lower-level loops distal to the source. Thus, this model offers a mechanism that can explain the stabilization of phenotypically diverse loopy network architectures in response to source pulsatility under physiologically relevant conditions and in the absence of other known loop stabilization mechanisms, such as random fluctuations in the load or perfusion homogenization.

nlin.AO↗

arXiv2025

Collective Behavior and Memory States in Flow Networks with Tunable Bistability

Multistability-induced hysteresis has been widely studied in mechanical systems, but such behavior has proven more difficult to reproduce experimentally in flow networks. Natural flow networks like animal and plant vasculature can exhibit complex nonlinear behavior to facilitate fluid transport, so multistable flows may inform their functionality. To probe such phenomena in an analogous model system, we utilize an electronic network of hysteretic bistable resistors designed to have tunable negative differential resistivity. We demonstrate our system's capability to generate complex global memory states in the form of voltage patterns, which is mediated by the tunable nonlinearity of each element's current-voltage characteristic. We investigate avalanching behavior arising from effective interactions, and demonstrate how to encode explicit interactions of arbitrary form by taking advantage of the tunable circuitry design.

cond-mat.soft↗

arXiv2025

The Geometry of Contraction-Induced Flows

Peristalsis is the driving mechanism behind a broad array of biological and engineered flows. In peristaltic pumping, a wave-like contraction of the tube wall produces local changes in volume which induce flow. Net flow arises due to geometric nonlinearities in the momentum equation, which must be properly captured to compute the flow accurately. While most previous models focus on radius-imposed peristalsis, they often neglect longitudinal length changes - a natural consequence of radial contraction in elastic materials. In this paper, to capture a more accurate picture of peristaltic pumping, we calculate the flow in an elastic vessel undergoing contractions in the transverse and longitudinal directions simultaneously, keeping the geometric nonlinearities arising in the strain. A careful analysis requires us to study our fluid using the Lagrangian coordinates of the elastic tube. We perform analytic calculations of the flow characteristics by studying the fluid inside a fixed boundary with time-dependent metric. This mathematical manipulation works even for large-amplitude contractions, as we confirm by comparing our analytical results to COMSOL simulations. We demonstrate that transverse and longitudinal contractions induce instantaneous flows at the same order in wall strain, but in opposite directions. We investigate the influence of the wall's Poisson ratio on the flow profile. Incompressible walls suppress flow by minimizing local volume changes, whereas auxetic walls enhance flow. For radius-imposed peristaltic waves, wall incompressibility reduces both reflux and particle trapping. In contrast, length-imposed waves typically generate backflow, although trapping can still occur at large amplitudes for some Poisson ratios. These results yield a more complete description of peristalsis in elastic media and offer a framework for studying contraction-induced flows more broadly.

physics.flu-dyn↗

arXiv2025

An efficient spectral method for the dynamic behavior of truss structures

Truss structures at macro-scale are common in a number of engineering applications and are now being increasingly used at the micro-scale to construct metamaterials. In analyzing the properties of a given truss structure, it is often necessary to understand how stress waves propagate through the system and/or its dynamic modes under time dependent loading so as to allow for maximally efficient use of space and material. This can be a computationally challenging task for particularly large or complex structures, with current methods requiring fine spatial discretization or evaluations of sizable matrices. Here we present a spectral method to compute the dynamics of trusses inspired by results from fluid flow networks. Our model accounts for the full dynamics of linearly elastic truss elements via a network Laplacian; a matrix object which couples the motions of the structure joints. We show that this method is equivalent to the continuum limit of linear finite element methods as well as capable of reproducing natural frequencies and modes determined by more complex and computationally costlier methods.

math.NA↗

arXiv2025

Self-construction and destruction of living transport networks

Biological transport networks adapt through dynamic interactions between material transport and structural modification during growth and development. In this work, we present a model of transport network growth driven by local material concentration. Using an advection-diffusion framework on a metric graph with a tip growth rule, we investigate how successive construction and destruction influence network development. Our results reveal that while repeated cycles of elongation and retraction can facilitate growth enhancement, a network need to have a structure that mitigates material dissipation. This finding suggests that additional regulatory mechanisms are necessary for networks to efficiently redistribute resources following structural retraction.

physics.bio-ph↗

arXiv2024

The central role of metabolism in vascular morphogenesis

As nutrients travel through microcirculation and are absorbed, their availability continuously decreases. However, a uniform nutrient distribution is critical, as it prevents tissue death in poorly supplied areas. How, then, do vascular networks achieve equi-perfusion? Given the extensive number of vessels in animal vascular systems, the structure of smaller vessels cannot be fully genetically predetermined and thus relies on a self-organizing developmental mechanism. We propose a simple, optimization-based adaptation rule to control vessel radii, aiming to equalize perfusion while minimizing flow resistance and material cost. This adaptation balances three competing factors: perfusion efficiency, energy dissipation, and material expenditure, which together drive complex network morphologies. These morphologies range from hierarchical architectures optimized for minimal resistance and material cost to dense networks that achieve efficient perfusion. Notably, metabolic demand is the primary factor modulating the transition between these contrasting structures. By applying our adaptation rule to networks in the rat mesentery and comparing the resulting optimal perfusion architectures with the experimental data, we can estimate the biologically relevant parameters, such as the oxygen absorption rate, for which the optimization and experimental networks align the most. Surprisingly, the optimal absorption rate we derive matches independent in vivo measurements. This suggests that reduced-order models like ours can provide valuable insights into the development and function of real vascular networks.

q-bio.TO↗

arXiv2024

Topological characterization of the continuum of allosteric response

Allosteric regulation in proteins is often accompanied by conformational changes that facilitate transmission of mechanical signals between distant ligand binding sites. Typically, these deformations are classified in terms of specific archetypes, including various types of hinge mechanisms or allosteric pathways localized to sequences of amino acids.However, many allosteric deformations resist such strict categorization. Here, we introduce a quantitative topological description of allosteric deformation, unifying all archetypal mechanisms into a single framework. The topological description aligns with two key structural features often associated with allosteric deformations, namely hinge domains and allosteric pathways, enabling us to quantify the significance of each of these features. To develop the analysis, we tune computer-generated mechanical networks to perform allostery-like functions, obtaining an ensemble of networks that establish a range of possible allosteric deformations. The analysis shows that these networks' allosteric mechanisms cannot be described in terms of discrete archetypes - they fall on a continuum. We then apply the same analysis to a collection of allosteric proteins with similar results, showing that our framework encompasses these proteins as well as designed allosteric networks. Our results provide a new picture for allostery, demonstrating not only how it can be described quantitatively, but also giving insight into how it emerges as a collective property.

cond-mat.soft↗

arXiv2023

Operating Principles of Peristaltic Pumping through a Dense Array of Valves

Immersed nonlinear elements are prevalent in biological systems that require a preferential flow direction, such as the venous and the lymphatic system. We investigate here a certain class of models where the fluid is driven by peristaltic pumping and the nonlinear elements are ideal valves that completely suppress backflow. This highly nonlinear system produces discontinuous solutions that are difficult to study. We show that as the density of valves increases, the pressure and flow are well-approximated by a continuum of valves which can be analytically treated, and we demonstrate through numeric simulation that the approximation works well even for intermediate valve densities. We find that the induced flow is linear in the peristaltic amplitude for small peristaltic forces and, in the case of sinusoidal peristalsis, is independent of pumping direction. Despite the continuum approximation used, the physical valve density is accounted for by modifying the resistance of the fluid appropriately. The suppression of backflow causes a net benefit in adding valves when the valve density is low, but once the density is high enough, valves predominately suppress forward flow, suggesting there is an optimum number of valves per wavelength. The continuum model for peristaltic pumping through an array of valves presented in this work can eventually provide insights about the design and operating principles of complex flow networks with a broad class of nonlinear elements.

physics.flu-dyn↗

arXiv2023

From localized to well-mixed: How commuter interactions shape disease spread

Interactions between commuting individuals can lead to large-scale spreading of rumors, ideas, or disease, even though the commuters have no net displacement. The emergent dynamics depend crucially on the commuting distribution of a population, that is how the probability to travel to a destination decays with distance from home. Applying this idea to epidemics, we will demonstrate the qualitatively different infection dynamics emerging from populations with different commuting distributions. If the commuting distribution is exponentially localized, we recover a reaction-diffusion system and observe Fisher waves traveling at a speed proportional to the characteristic commuting distance. If the commuting distribution has a long tail, then no finite-velocity waves can form, but we show that, in some regimes, there is nontrivial spatial dependence that the well-mixed approximation neglects. We discuss how, in all cases, an initial dispersal-dominated regime can allow the disease to go undetected for a finite amount of time before exponential growth takes over. This "offset time" is a quantity of huge importance for epidemic surveillance and yet largely ignored in the literature.

q-bio.PE↗

arXiv2023

Universal scaling of shear thickening transitions

Nearly all dense suspensions undergo dramatic and abrupt thickening transitions in their flow behaviour when sheared at high stresses. Such transitions occur when the dominant interactions between the suspended particles shift from hydrodynamic to frictional. Here, we interpret abrupt shear thickening as a precursor to a rigidity transition and give a complete theory of the viscosity in terms of a universal crossover scaling function from the frictionless jamming point to a rigidity transition associated with friction, anisotropy, and shear. Strikingly, we find experimentally that for two different systems -- cornstarch in glycerol and silica spheres in glycerol -- the viscosity can be collapsed onto a single universal curve over a wide range of stresses and volume fractions. The collapse reveals two separate scaling regimes, due to a crossover between frictionless isotropic jamming and frictional shear jamming, with different critical exponents. The material-specific behaviour due to the microscale particle interactions is incorporated into a scaling variable governing the proximity to shear jamming that depends on both stress and volume fraction. This reformulation opens the door to importing the vast theoretical machinery developed to understand equilibrium critical phenomena to elucidate fundamental physical aspects of the shear thickening transition.

cond-mat.soft↗

arXiv2023

The fluidic memristor: collective phenomena in elastohydrodynamic networks

Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a highly non-linear pressure-flow relationship. Although we understand how these elements work individually, their collective behavior remains poorly understood. In this work, we combine experiments, theory, and numerical simulations to understand the main mechanisms underlying the collective behavior of soft flow networks with elements that exhibit negative differential resistance. Strikingly, our theoretical analysis and experiments reveal that a minimal network of nonlinear resistors, which we have termed a `fluidic memristor', displays history-dependent resistance. This new class of element can be understood as a collection of hysteresis loops that allows this fluidic system to store information. Our work provides insights that may inform new applications of fluid flow networks in soft materials science, biomedical settings, and soft robotics, and may also motivate new understanding of the flow networks involved in animal and plant physiology.

cond-mat.soft↗

arXiv2023

Exact solutions for the wrinkle patterns of confined elastic shells

Complex textured surfaces occur in nature and industry, from fingerprints to lithography-based micropatterns. Wrinkling by confinement to an incompatible substrate is an attractive way of generating reconfigurable patterned topographies, but controlling the often asymmetric and apparently stochastic wrinkles that result remains an elusive goal. Here, we describe a new approach to understanding the wrinkles of confined elastic shells, using a Lagrange multiplier in place of stress. Our theory reveals a simple set of geometric rules predicting the emergence and layout of orderly wrinkles, and explaining a surprisingly generic co-existence of ordered and disordered wrinkle domains. The results agree with numerous test cases across simulation and experiment and represent an elementary geometric toolkit for designing complex wrinkle patterns.

cond-mat.soft↗

arXiv2023

A Braess' paradox analog in physical networks of optimal exploration

In the stochastic exploration of geometrically embedded graphs, intuition suggests that providing a shortcut between a pair of nodes reduces the mean first passage time of the entire graph. Counterintuitively, we find a Braess paradox analog. For regular diffusion, shortcuts can worsen the overall search efficiency of the network, although they bridge topologically distant nodes. We propose an optimization scheme under which each edge adapts its conductivity to minimize the graph's search time. The optimization reveals a relationship between the structure and diffusion exponent and a crossover from dense to sparse graphs as the exponent increases.

nlin.AO↗

arXiv2023

Tradeoffs between energy efficiency and mechanical response in fluid flow networks

Transport networks are typically optimized, either by evolutionary pressures in biological systems or by human design in engineered structures. In the case of systems such as the animal vasculature, the transport of fluids is hindered by the inherent viscous resistance to flow while being kept in a dynamic state by the pulsatile nature of the heart and elastic properties of the vessel walls. While this imparted pulsatility necessarily increases the dissipation of energy caused by the resistance, the vessel elasticity helps to reduce overall dissipation by attenuating the amplitude of the pulsatile components of the flow. However, we find that this reduction in energy loss comes at the price of increasing the time required to respond to changes in the flow boundary conditions for vessels longer than a critical size. In this regime, dissipation and response time are found to follow a simple power law scaling relation both in single vessels as well as hierarchically structured networks. We validate the model in human vasculature and apply biologically relevant parameters to show that response time has to be considered alongside dissipation as an important fitness cost function in the evolutionary optimization of animal vascular networks.

physics.bio-ph↗

arXiv2022

Pulsatile Driving Stabilizes Loops in Elastic Flow Networks

Existing models of adaptation in biological flow networks consider their constituent vessels (e.g. veins and arteries) to be rigid, thus predicting a non physiological response when the drive (e.g. the heart) is dynamic. Here we show that incorporating pulsatile driving and properties such as fluid inertia and vessel compliance into a general adaptation framework fundamentally changes the expected structure at steady state of a minimal one-loop network. In particular, pulsatility is observed to give rise to resonances which can stabilize loops for a much broader class of metabolic cost functions than predicted by existing theories. Our work points to the need for a more realistic treatment of adaptation in biological flow networks, especially those driven by a pulsatile source, and provides insights into pathologies that emerge when such pulsatility is disrupted in human beings.

nlin.AO↗