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We give a brief overview of the seminal paper which introduced the Kardar-Parisi-Zhang equation as a paradigmatic model for random growth in 1986. We describe some of the developments to which it gave rise in mathematics and physics over the years, and some examples of applications.
The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data - such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter - density is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects' collective behavior.
We study the Hegselmann-Krause model of opinion dynamics on sparse, unbiased networks generated via Wilson's algorithm, unveiling how network connectivity and confidence bounds jointly determine collective behavior. By systematically exploring the parameter space spanned by the confidence level $ε$ and the mean degree density $μ$, we construct comprehensive phase diagrams that classify the emergent steady states into different degrees of fragmentation and consensus. We uncover a nonmonotonic re-entrant transition where increased connectivity can paradoxically suppress consensus, and show that full unanimity is unattainable at low connectivity due to structural isolation. Convergence times exhibit two distinct slowdowns: a finite-size, connectivity-dependent resonance near $ε\sim 1/N$, and a critical peak associated with the established fragmentation-to-consensus transition. While the critical confidence threshold $ε_c$ stabilizes near 0.2 for large system sizes, finite-size effects and sparse connectivity significantly alter the dynamics and phase boundaries in smaller populations. Our results offer new insights into the interplay between network topology and opinion dynamics, and highlight conditions under which increased connectivity may hinder, rather than promote, consensus.
Ising machines (IMs) are specialized devices designed to efficiently solve combinatorial optimization problems. Among such problems, Boolean Satisfiability (SAT) is particularly relevant in industrial applications. To solve SAT problems using IMs, it is crucial to incorporate higher-order interactions. However, in analog IMs, interactions of different orders scale unevenly with the continuous spin amplitudes, introducing imbalances that can significantly degrade performance. We present a numerical comparison of methods to mitigate these imbalances, evaluating time-to-solution and success rate on Uniform Random 3-SAT instances from the SATLIB benchmark set. Our results show that the most effective approach employs spin interactions that are proportional to the signs of spins, rather than their continuous amplitudes. This generalizes our previous work, which showed that such interactions best mitigate imbalances induced by external fields in quadratic analog IMs. In this work, its advantage becomes substantially more pronounced, as it naturally mitigates imbalances across all interaction orders. We further demonstrate that smooth approximations of this method make it compatible with analog hardware. Our findings underscore the central role of spin-sign-based interactions in enabling robust and scalable analog IM dynamics.
Many combinatorial optimization problems (COPs) are naturally expressed using variables that take on more than two discrete values. To solve such problems using Ising machines (IMs) - specialized analog or digital devices designed to solve COPs efficiently - these multi-valued integers must be encoded using binary spin variables. A common approach is one-hot encoding, where each variable is represented by a group of spins constrained so that exactly one spin is in the "up" state. However, this encoding introduces energy barriers: changing an integer's value requires flipping two spins and passing through an invalid intermediate state. This creates rugged energy landscapes that may hinder optimization. We propose a higher-order Ising formulation for Max-3-Cut, which is the smallest fundamental COP with multi-valued integer variables. Our formulation preserves valid configurations under single-spin updates. The resulting energy landscapes are smoother, and we show that this remains true even when the binary variables are relaxed to continuous values, making it well-suited for analog IMs as well. Benchmarking on such an IM, we find that the higher-order formulation leads to significantly faster solutions than the Ising baseline. Interestingly, we find that an empirical rescaling of some terms in the Ising formulation - a heuristic proposed in prior work - approaches the performance of the higher-order Ising formulation, underscoring the importance of empirical parameter tuning in COP encodings.
Continuous cellular automata (CCAs) have evolved from discrete lookup tables to continuous partial differential equation (PDE) formulations in the search for novel forms of complexity. Despite innovations in qualitative behavior, analytical methods have lagged behind, reinforcing the notion that emergent complexity defies simple explanation. In this paper, we demonstrate that the PDE formulation of Asymptotic Lenia enables rigorous analysis using dynamical systems theory. We apply the concepts of symmetries, attractors, Lyapunov exponents, and fractal dimensions to characterize complex behaviors mathematically. Our contributions include: (1) a mathematical explanation for the four distinct solution classes (solitons, rotators, periodic and chaotic patterns), (2) conditions for the existence of a global attractor with fractal dimension $>4$, (3) identification of Kaplan-Yorke dimension as an effective complexity measure for CCAs, and (4) an efficient open-source implementation for calculating Lyapunov exponents and the covariant Lyapunov vectors for CCAs. We conclude by identifying the minimal set of properties that enable complex behavior in a broader class of CCAs. This framework provides a foundation for understanding and measuring complexity in artificial life systems.
Lenia is a continuous extension of Conway's Game of Life that exhibits rich pattern formations including self-propelling structures called gliders. In this paper, we focus on Asymptotic Lenia, a variant formulated as partial differential equations. By utilizing this mathematical formulation, we analytically derive the conditions for glider patterns, which we term the ``Glider Equation.'' We demonstrate that by using this equation as a loss function, gradient descent methods can successfully discover stable glider configurations. This approach enables the optimization of update rules to find novel gliders with specific properties, such as faster-moving variants. We also derive a velocity-free equation that characterizes gliders of any speed, expanding the search space for novel patterns. While many optimized patterns result in transient gliders that eventually destabilize, our approach effectively identifies diverse pattern formations that would be difficult to discover through traditional methods. Finally, we establish connections between Asymptotic Lenia and neural field models, highlighting mathematical relationships that bridge these systems and suggesting new directions for analyzing pattern formation in continuous dynamical systems.
Spontaneous self-replication in cellular automata has long been considered rare, with most known examples requiring careful design or artificial initialization. In this paper, we present formal, causal evidence that such replication can emerge unassisted -- and that it can do so in a distributed, multi-component form. Building on prior work identifying complex dynamics in the Outlier rule, we introduce a data-driven framework that reconstructs the full causal ancestry of patterns in a deterministic cellular automaton. This allows us to rigorously identify self-replicating structures via explicit causal lineages. Our results show definitively that self-replicators in the Outlier CA are not only spontaneous and robust, but are also often composed of multiple disjoint clusters working in coordination, raising questions about some conventional notions of individuality and replication in artificial life systems.
Elementary Cellular Automata (ECAs) exhibit diverse behaviours often categorized by Wolfram's qualitative classification. To provide a quantitative basis for understanding these behaviours, we investigate the global dynamics of such automata and we describe a method that allows us to compute the number of all configurations leading to short attractors in a limited number of time steps. This computation yields exact results in the thermodynamic limit (as the CA grid size grows to infinity), and is based on the Transfer Matrix Method (TMM) that we adapt for our purposes. Specifically, given two parameters $(p, c)$ we are able to compute the entropy of all initial configurations converging to an attractor of size $c$ after $p$ time-steps. By calculating such statistics for various ECA rules, we establish a quantitative connection between the entropy and the qualitative Wolfram classification scheme. Class 1 rules rapidly converge to maximal entropy for stationary states ($c=1$) as $p$ increases. Class 2 rules also approach maximal entropy quickly for appropriate cycle lengths $c$, potentially requiring consideration of translations. Class 3 rules exhibit zero or low finite entropy that saturates after a short transient. Class 4 rules show finite positive entropy, similar to some Class 3 rules. This method provides a precise framework for quantifying trajectory statistics, although its exponential computational cost in $p+c$ restricts practical analysis to short trajectories.
This paper presents a traffic state estimation (TSE) method in congestion for distributed fiber-optic sensing (DFOS). DFOS detects vehicle driving vibrations along the optical fiber and obtains their trajectories in the spatiotemporal plane. From these trajectories, DFOS provides mean velocities for real-time spatially continuous traffic monitoring without dead zones. However, when vehicle vibration intensities are insufficiently low due to slow speed, trajectories cannot be obtained, leading to missing values in mean velocity data. It restricts DFOS applicability in severe congestion. Therefore, this paper proposes a missing value imputation method based on data assimilation. Our proposed method is validated on two expressways in Japan with the reference data. The results show that the mean absolute error (MAE) of the imputed mean velocities to the reference increases only by 1.5 km/h as compared with the MAE of non-missing values. This study enhances the wide-range applicability of DFOS in practical cases.
Boolean networks are a widely used modeling framework in systems biology for studying gene regulation, signal transduction, and cellular decision-making. Empirical studies indicate that biological Boolean networks exhibit a high degree of canalization, a structural property of Boolean update rules that stabilizes dynamics and constrains state transitions. Despite its central role, existing software packages provide limited support for the systematic generation of Boolean functions and networks with prescribed canalization properties. We present BoolForge, a Python toolbox for the random generation and analysis of Boolean functions and networks, with a particular focus on canalization. BoolForge enables users to (i) generate random Boolean functions with specified canalizing depth, layer structure, and related constraints; (ii) construct Boolean networks with tunable topological and functional properties; and (iii) analyze structural and dynamical features including canalization measures, robustness, modularity, and attractor structure. By enabling controlled generation alongside analysis, BoolForge facilitates ensemble-based investigations of structure-dynamics relationships, benchmarking of theoretical predictions, and construction of biologically informed null models for Boolean network studies. Availability and Implementation: BoolForge is implemented in Python ($\geq$3.10) and can be installed via \texttt{pip install boolforge}. Source code and documentation are available at https://github.com/ckadelka/BoolForge. A PDF tutorial compendium is provided as Supplementary Material.
In previous works, we introduced the notion of dominant vertices in the context of dynamical systems on networks. This is a set of nodes in the underlying network whose evolution determines the whole network's dynamics after a transient time. In this paper, we focus on the case of Boolean networks. We define a reduced graph on the dominant vertices and an induced dynamics on this graph, which we prove is asymptotically equivalent to the original Boolean dynamics. Asymptotic conjugacy ensures that the systems, restricted to their respective attractors, are dynamically equivalent. For a significant class of networks, the induced dynamics is indeed a reduction of the original system. In these cases, the reduction, which is obtained from the structure of dominant vertices, supplies a more tractable system with the same structure of attractors as the original one. Furthermore, the structure of the induced system allows us to establish bounds on the number and period of the attractors, as well as on the reduction of the basin's sizes and transient lengths. We illustrate this reduction by considering a class of networks, which we call clover networks, whose dominant set is a singleton. To get insight into the structure of the basins of attraction of Boolean networks with a single dominant vertex, we complement this work with a numerical exploration of the behavior of a parametrized ensemble of systems of this kind.
We introduce families of classical stochastic dynamics in two and higher dimensions which stabilize order in the absence of any symmetry. Our dynamics are qualitatively distinct from Toom's rule, and have the unusual feature of being fluctuation-stabilized: their order becomes increasingly fragile in larger dimensions. One of our models maintains an ordered phase only in two dimensions. The phase transitions that occur as the order is lost appear to realize new dynamical universality classes which are fundamentally non-equilibrium in character.
We introduce the Stochastic Box-Ball System (SBBS), a probabilistic cellular automaton that generalizes the classic Takahashi-Satsuma Box-Ball System. In SBBS, particles are transported by a carrier with a fixed capacity that may fail to pick up any given particle with a fixed probability $ε$. This model interpolates between two known integrable systems: the Box-Ball System (as $ε\rightarrow 0$) and the PushTASEP (as $ε\rightarrow 1$). We show that the long-term behavior of SBBS is governed by isolated particles and the occasional emergence of short solitons, which can form longer solitons but are more likely to fall apart. More precisely, we first show that all particles are isolated except for a $1/\sqrt{n}$-fraction of times in any given $n$ steps, and solitons keep forming for this fraction of times. We then show that under diffusive scaling, both SBBS (for any carrier capacity) and PushTASEP converge weakly to semimartingale reflecting Brownian Motions (SRBMs) on the Weyl chamber with explicit covariance and reflection matrices, which are consistent with the microscale relations between these systems. The reflection matrix for SBBS is determined by how 2-solitons behave and exhibit ``solitonic bias'' visible in the diffusive scale. Our proof relies on a new, extended SRBM invariance principle that we develop in this work. This principle can handle processes with complex boundary behavior that can be written as "overdetermined" Skorokhod decompositions, which is crucial for analyzing the complex solitonic interaction in SBBS. We believe this tool may be of independent interest.
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle distribution functions (PDFs). Despite its success, solving the LBE numerically remains computationally intensive due to strict time-step restrictions imposed by collision kernels. Here, we introduce a physics-informed neural operator framework for the LBE that enables prediction over large time horizons without step-by-step integration, effectively bypassing the need to explicitly solve the collision kernel. We incorporate intrinsic moment-matching constraints of the LBE, along with global equivariance of the full distribution field, enabling the model to capture the complex dynamics of the underlying kinetic system. Our framework is discretization-invariant, enabling models trained on coarse lattices to generalise to finer ones (kinetic super-resolution). In addition, it is agnostic to the specific form of the underlying collision model, which makes it naturally applicable across different kinetic datasets regardless of the governing dynamics. Our results demonstrate robustness across complex flow scenarios, including von Karman vortex shedding, ligament breakup, and bubble adhesion. This establishes a new data-driven pathway for modelling kinetic systems.
Fuzzy disjunctive normal form (FDNF) gives the canonical multi-affine extension of an elementary cellular automaton (ECA) rule to the unit cube. Although it preserves the Boolean rule on binary states, its multi-affine structure can smooth high-contrast CA patterns and restrict continuous-state dynamics. We introduce generalized FDNF rules \[ \widetilde f_k^{g,u,v,w}(x,y,z) = g\left(f_k(u(x),v(y),w(z))\right), \] where the transformations $g,u,v,w: [0,1] \to [0,1]$ fix the endpoints. The identity maps recover ordinary FDNF, while threshold-like, discontinuous, non-monotone, and expanding choices yield rule-preserving fuzzy ECAs. We demonstrate, in representative rules, that the transformation shape strongly affects pattern dynamics: threshold-like maps promote ECA-like pattern recovery, parameter deformations interpolate toward FDNF-like smoothing, and discontinuities induce gap-generated regimes. Pattern changes are summarized by contrast, fuzziness, and a finite-resolution participation-type support exponent. In three-cell systems, an expanding non-monotone transformation yields stable period-six cycles for rule 210, verified by interval arithmetic, coexisting with an expanding invariant line set; rules 51 and 85 inherit one-dimensional expanding dynamics. The framework provides a rule-preserving bridge from Boolean cellular automata to fuzzy and continuous-state nonlinear dynamics.
We develop a Koopman operator framework for studying the {computational properties} of dynamical systems. Specifically, we show that the resolvent of the Koopman operator provides a natural abstraction of halting, yielding a ``Koopman halting problem that is recursively enumerable in general. For symbolic systems, such as those defined on Cantor space, this operator formulation captures the reachability between clopen sets, while for equicontinuous systems we prove that the Koopman halting problem is decidable. Our framework demonstrates that absorbing (halting) states {in finite automata} correspond to Koopman eigenfunctions with eigenvalue one, while cycles in the transition graph impose algebraic constraints on spectral properties. These results provide a unifying perspective on computation in symbolic and analog systems, showing how computational universality is reflected in operator spectra, invariant subspaces, and algebraic structures. Beyond symbolic dynamics, this operator-theoretic lens opens pathways to analyze {computational power of} a broader class of dynamical systems, including polynomial and analog models, and suggests that computational hardness may admit dynamical signatures in terms of Koopman spectral structure.
We refine an old idea for performing fault-tolerant error correction in topological codes by simulating confining interactions between excitations. We implement confinement using an array of local classical processors that measure syndromes, broadcast messages to neighboring processors, and move excitations using received messages. The dynamics of the resulting real-time decoder is geometrically local, homogeneous in spacetime, and self-organized, operating without any form of global control. We prove that below a threshold error rate, it achieves a memory lifetime scaling as a stretched exponential in the linear system size $L$, provided that it has access to $O({\rm polylog}(L))$ noiseless classical bits for each noisy qubit. When applied to the surface code subject to depolarizing noise and measurement errors of equal strength, numerics indicate a threshold at $p_c \approx 1.5\%$.