arXiv subjects

Jan Haskovec

Publications and source records attributed to Jan Haskovec.

At least 19 recordsLinked to original sources

arXiv2026

Gradient Flow Structure of the Spontaneous Aggregation Model

We identify a previously unnoticed gradient-flow structure of the Fokker--Planck equation arising in the spontaneous particle aggregation model. For an exponential response function, the equation is a generalized Wasserstein gradient flow of the attractive McKean--Vlasov free energy with a nonlocal mobility. We show that, within a natural class of local entropies and symmetric interaction energies, this structure essentially singles out the exponential response. We discuss consequences for stationary states, global minimizers, the single-cluster structure in one dimension, and formally outline convergence to equilibrium using a Wasserstein--\mbox{\L}ojasiewicz inequality.

math.AP↗

arXiv2026

Memory Stabilizes Spontaneous Particle Aggregation: A Linearized Vlasov--Fokker--Planck Analysis

We perform a linearized stability analysis of the Vlasov--Fokker--Planck equation obtained as the mean-field description of a stochastic spontaneous aggregation model with memory. Memory is represented by a chain of $K$ internal variables. We characterize the spatially homogeneous equilibria and derive a scalar dispersion relation for spatially inhomogeneous perturbations. When all relaxation rates are equal, we show that every Fourier mode that is unstable in the memoryless model possesses a unique critical relaxation parameter: sufficiently long memory stabilizes the mode, whereas it remains unstable for short memory. Although memory may destabilize individual modes associated with negative Fourier coefficients of the sensing kernel, we prove that, for radially symmetric distance-decreasing kernels, it cannot destabilize a homogeneous equilibrium that is stable in the memoryless model. Thus, memory cannot create an overall instability of an otherwise stable homogeneous state, although it may change the set of the unstable modes. We present a numerical example for the normalized top-hat kernel, demonstrating that increasing the effective memory length successively stabilizes the Fourier modes, with higher frequencies being stabilized before lower ones. This is consistent with the coarsening effect observed in recent particle simulations. Finally, under suitable nondegeneracy and regularity assumptions, we use the Crandall--Rabinowitz theorem to obtain branches of spatially inhomogeneous stationary solutions bifurcating from the homogeneous state.

math.AP↗

arXiv2026

A duality approach to the dense graph limit for biological transportation networks

We develop a duality-based formulation of the dense graph limit for a variational model of biological transportation networks, where edge conductivities balance pumping power against metabolic cost. In contrast to the pressure-based approach of our previous work, which required conductivities to be uniformly positive, the present formulation allows general nonnegative conductivity kernels. The kinetic energy is defined through a dual variational principle, which remains meaningful for degenerate integrable kernels and assigns infinite energy when the associated nonlocal Poisson problem is not solvable. Using this formulation, we prove $\Gamma$-convergence in the sense of Mosco of the semidiscrete network energies to a continuum energy on symmetric nonnegative kernels. The convergence is obtained in the natural $L^\gamma$ topology dictated by the metabolic term. The $\Gamma$-$\liminf$ inequality follows directly from the dual formulation, while $\Gamma$-$\limsup$ recovery sequences are constructed by positive regularization of the conductivity kernels.

math.OC↗

arXiv2026

Consensus and flocking with transmission and reaction delays

We investigate consensus formation and flocking behavior in multi-agent systems subject to two distinct types of delays: a transmission delay accounting for information exchange between agents, and a reaction delay representing the processing time before agents adjust their states. For a simplified linear two-agent system, we provide explicit insight into how these delays affect asymptotic stability. We then derive sufficient conditions for asymptotic consensus and flocking in the general multi-agent setting with a nonlinear, globally positive influence function. These conditions require the delays to be sufficiently small relatively to the initial data and the decay rate of the influence function. The analysis is based on a Lyapunov functional approach combined with a Halanay-type inequality. Our results establish rigorous conditions under which collective behavior emerges in delayed multi-agent systems where both communication and reaction lags are non-negligible, with applications to biological, social, and engineered systems.

math.DS↗

arXiv2025

Impact of memory on clustering in spontaneous particle aggregation

The effect of short-term and long-term memory on spontaneous aggregation of organisms is investigated using a stochastic agent-based model. Each individual modulates the amplitude of its random motion according to the perceived local density of neighbors. Memory is introduced via a chain of $K$~internal variables that allow agents to retain information about previously encountered densities. The parameter $K$ controls the effective length of memory. A formal mean-field limit yields a macroscopic Fokker--Planck equation, which provides a continuum description of the system in the large-population limit. Steady states of this equation are characterized to interpret the emergence and morphology of clusters. Systematic stochastic simulations in one- and two-dimensional spatial domains reveal that short- or moderate-term memory promotes coarsening, resulting in a smaller number of larger clusters, whereas long-term memory inhibits aggregation and increases the proportion of isolated individuals. Statistical analysis demonstrates that extended memory reduces the agents' responsiveness to environmental stimuli, explaining the transition from aggregation to dispersion as $K$ increases. These findings identify memory as a key factor controlling the collective organization of self-driven agents and provide a bridge between individual-level dynamics and emergent spatial patterns.

math.DS↗

arXiv2025

Gradient Flows for the $p$-Laplacian Arising from Biological Network Models: A Novel Dynamical Relaxation Approach

We investigate a scalar partial differential equation model for the formation of biological transportation networks. Starting from a discrete graph-based formulation on equilateral triangulations, we rigorously derive the corresponding continuum energy functional as the $\Gamma$-limit under network refinement and establish the existence of global minimizers. The model possesses a gradient-flow structure whose steady states coincide with solutions of the $p$-Laplacian equation. Building on this connection, we implement finite element discretizations and propose a novel dynamical relaxation scheme that achieves optimal convergence rates in manufactured tests and exhibits mesh-independent performance, with the number of time steps, nonlinear iterations, and linear solves remaining stable under uniform mesh refinement. Numerical experiments confirm both the ability of the scalar model to reproduce biologically relevant network patterns and its effectiveness as a computationally efficient relaxation strategy for solving $p$-Laplacian equations for large exponents $p$.

math.AP↗

arXiv2025

High-friction limit for bipolar Euler-Riesz systems

We consider a bipolar Euler-Riesz system and rigorously justify the high-friction limit of weak solutions towards a bipolar aggregation-diffusion system with Riesz interactions. The analysis is carried out via the relative entropy method in the regime where smooth solutions of the limiting equations exist. This extends previous results on the high-friction limit of bipolar Euler-Poisson systems to a more general class of interactions, and extends the one-species Euler-Riesz case to the bipolar setting.

math.AP↗

arXiv2025

Rigorous dense graph limit of a model for biological transportation networks

We rigorously derive the dense graph limit of a discrete model describing the formation of biological transportation networks. The discrete model, defined on undirected graphs with pressure-driven flows, incorporates a convex energy functional combining pumping and metabolic costs. It is constrained by a Kirchhoff law reflecting the local mass conservation. We first rescale and reformulate the discrete energy functional as an integral `semi-discrete' functional, where the Kirchhoff law transforms into a nonlocal elliptic integral equation. Assuming that the sequence of graphs is uniformly connected and that the limiting graphon is 0-1 valued, we prove two results: (1) rigorous Gamma-convergence of the sequence of the semi-discrete functionals to a continuum limit as the number of graph nodes and edges tends to infinity; (2) convergence of global minimizers of the discrete functionals to a global minimizer of the limiting continuum functional. Our results provide a rigorous mathematical foundation for the continuum description of biological transport structures emerging from discrete networks.

math.OC↗

arXiv2025

Asymptotic consensus with transmission and reaction delay: an overview

The aim of this paper is to provide a systematic overview of results on asymptotic consensus for the Hegselmann-Krause-type model with delay and discuss the corresponding analytical tools. We explain that two types (sources) of delay - transmission and reaction - are justifiable from the modeling point of view. We consider both classical and normalized communication weights. Studying a toy model with two agents only, we develop an intuitive insight into what type of dynamics we can expect from the systems. In particular, we stress that with transmission-type delay, asymptotic consensus can be reached with any length of the delay (i.e., without smallness assumptions). In contrast, the systems with reaction-type delay can only reach asymptotic consensus if the delay is sufficiently small. We formulate four theorems that establish asymptotic consensus in the following scenarios: (1) transmission-type delay with classical communication weights, (2) transmission-type delay with normalized communication weights, (3) reaction-type delay with symmetric communication weights, (4) reaction-type delay with non-symmetric communication weights. We explain how the methods of proof depend on the particular scenario: direct estimates for (1), convexity arguments for (2), Lyapunov functional for (3) and generalized Gronwall-Halanay inequality for (4).

math.DS↗

arXiv2025

Robust and scalable nonlinear solvers for finite element discretizations of biological transportation networks

We develop robust and scalable fully implicit nonlinear finite element solvers for the simulations of biological transportation networks driven by the gradient flow minimization of a non-convex energy cost functional. Our approach employs a discontinuous space for the conductivity tensor that allows us to guarantee the preservation of its positive semi-definiteness throughout the entire minimization procedure arising from the time integration of the gradient flow dynamics using a backward Euler scheme. Extensive tests in two and three dimensions demonstrate the robustness and performance of the solver, highlight the sensitivity of the emergent network structures to mesh resolution and topology, and validate the resilience of the linear preconditioner to the ill-conditioning of the model. The implementation achieves near-optimal parallel scaling on large-scale, high-performance computing platforms. To the best of our knowledge, the network formation system has never been simulated in three dimensions before. Consequently, our three-dimensional results are the first of their kind.

cs.CE↗

arXiv2024

Measure-based approach to mesoscopic modeling of optimal transportation networks

We propose a mesoscopic modeling framework for optimal transportation networks with biological applications. The network is described in terms of a joint probability measure on the phase space of tensor-valued conductivity and position in physical space. The energy expenditure of the network is given by a functional consisting of a pumping (kinetic) and metabolic power-law term, constrained by a Poisson equation accounting for local mass conservation. We establish convexity and lower semicontinuity of the functional on approriate sets. We then derive its gradient flow with respect to the 2-Wasserstein topology on the space of probability measures, which leads to a transport equation, coupled to the Poisson equation. To lessen the mathematical complexity of the problem, we derive a reduced Wasserstein gradient flow, taken with respect to the tensor-valued conductivity variable only. We then construct equilibrium measures of the resulting PDE system. Finally, we derive the gradient flow of the constrained energy functional with respect to the Fisher-Rao (or Hellinger-Kakutani) metric, which gives a reaction-type PDE. We calculate its equilibrium states, represented by measures concentrated on a hypersurface in the phase space.

math.AP↗

arXiv2023

Numerical approach to centrality of optimal transportation networks

We study hierarchical properties of optimal transportation networks with biological background. The networks are obtained as minimizers of an energy functional which involves a metabolic cost term of a power-law form with exponent $\gamma>0$. In the range $\gamma\in (0,1)$, most relevant for biological applications, the functional is non-convex and its local minima correspond to loop-free graphs (trees). We propose a numerical scheme that performs energy descent by searching the discrete set of local minimizers, combined with a Monte-Carlo approach. We verify the performance of the scheme in the borderline case $\gamma=1$, where the functional is convex. For~a~particular example of a leaf-shaped planar graph, we evaluate the global reaching centrality (GRC) of the (local) minimizers in dependence on the value of $\gamma\in (0,1]$. We observe that the GRC, which can be understood as a measure of hierarchical organization of the graph, monotonically increases with increasing $\gamma$. To our best knowledge, this is the first quantification of the influence of the value of the metabolic exponent on the hierarchical organization of the (almost) optimal transportation network.

math.OC↗

arXiv2023

Robust network formation with biological applications

We provide new results on the structure of optimal transportation networks obtained as minimizers of an energy cost functional consisting of a kinetic (pumping) and material (metabolic) cost terms, constrained by a local mass conservation law. In particular, we prove that every tree (i.e., graph without loops) represents a local minimizer of the energy with concave metabolic cost. For the linear metabolic cost, we prove that the set of minimizers contains a loop-free structure. Moreover, we enrich the energy functional such that it accounts also for robustness of the network, measured in terms of the Fiedler number of the graph with edge weights given by their conductivities. We examine fundamental properties of the modified functional, in particular, its convexity and differentiability. We provide analytical insights into the new model by considering two simple examples. Subsequently, we employ the projected subgradient method to find global minimizers of the modified functional numerically. We then present two numerical examples, illustrating how the optimal graph's structure and energy expenditure depend on the required robustness of the network.

math.OC↗

arXiv2023

Self-regulated biological transportation structures with general entropy dissipations, part I: the 1D case

We study self-regulating processes modeling biological transportation networks as presented in \cite{portaro2023}. In particular, we focus on the 1D setting for Dirichlet and Neumann boundary conditions. We prove an existence and uniqueness result under the assumption of positivity of the diffusivity $D$. We explore systematically various scenarios and gain insights into the behavior of $D$ and its impact on the studied system. This involves analyzing the system with a signed measure distribution of sources and sinks. Finally, we perform several numerical tests in which the solution $D$ touches zero, confirming the previous hints of local existence in particular cases.

math.AP↗

arXiv2023

Asymmetry and condition number of an elliptic-parabolic system for biological network formation

We present results of numerical simulations of the tensor-valued elliptic-parabolic PDE model for biological network formation. The numerical method is based on a non-linear finite difference scheme on a uniform Cartesian grid in a 2D domain. The focus is on the impact of different discretization methods and choices of regularization parameters on the symmetry of the numerical solution. In particular, we show that using the symmetric alternating-direction implicit (ADI) method for time discretization helps preserve the symmetry of the solution, compared to the (non symmetric) ADI method. Moreover, we study the effect of regularization by isotropic background permeability $r>0$, showing that increased condition number of the elliptic problem due to decreasing value of $r$ leads to loss of symmetry. We show that in this case, neither the use of the symmetric ADI method preserves the symmetry of the solution. Finally, we perform numerical error analysis of our method making use of Wasserstein distance.

math.NA↗

arXiv2023

Optimal condition for asymptotic consensus in the Hegselmann-Krause model with finite speed of information propagation

We prove that asymptotic global consensus is always reached in the Hegselmann-Krause model with finite speed of information propagation $\mathfrak{c}>0$ under minimal (i.e., necessary) assumptions on the influence function. In particular, we assume that the influence function is globally positive, which is necessary for reaching global consensus, and such that the agents move with speeds strictly less than $\mathfrak{c}$, which is necessary for well-posedness of solutions. From this point of view, our result is optimal. The proof is based on the fact that the state-dependent delay, induced by the finite speed of information propagation, is uniformly bounded.

math.AP↗

arXiv2022

Comparison of two aspects of a PDE model for biological network formation

We compare the solutions of two systems of partial differential equations (PDE), seen as two different interpretations of the same model that describes formation of complex biological networks. Both approaches take into account the time evolution of the medium flowing through the network, and we compute the solution of an elliptic-parabolic PDE system for the conductivity vector $m$, the conductivity tensor $\mathbb{C}$ and the pressure $p$. We use finite differences schemes in a uniform Cartesian grid in the spatially two-dimensional setting to solve the two systems, where the parabolic equation is solved by a semi-implicit scheme in time. Since the conductivity vector and tensor appear also in the Poisson equation for the pressure $p$, the elliptic equation depends implicitly on time. For this reason we compute the solution of three linear systems in the case of the conductivity vector $m\in\mathbb{R}^2$, and four linear systems in the case of the symmetric conductivity tensor $\mathbb{C}\in\mathbb{R}^{2\times 2}$, at each time step. To accelerate the simulations, we make use of the Alternating Direction Implicit (ADI) method. The role of the parameters is important for obtaining detailed solutions. We provide numerous tests with various values of the parameters involved, to see the differences in the solutions of the two systems.

math.NA↗

arXiv2022

Emergence of biological transportation networks as a self-regulated process

We study self-regulating processes modeling biological transportation networks. Firstly, we write the formal $L^2$-gradient flow for the symmetric tensor valued diffusivity $D$ of a broad class of entropy dissipations associated with a purely diffusive model. The introduction of a prescribed electric potential leads to the Fokker-Planck equation, for whose entropy dissipations we also investigate the formal $L^2$-gradient flow. We derive an integral formula for the second variation of the dissipation functional, proving convexity (in dependence of diffusivity tensor) for a quadratic entropy density modeling Joule heating. Finally, we couple in the Poisson equation for the electric potential obtaining the Poisson-Nernst-Planck system. The formal gradient flow of the associated entropy loss functional is derived, giving an evolution equation for $D$ coupled with two auxiliary elliptic PDEs.

math.AP↗