39 papers
This paper combines non-linear shock wave theory with frame-by-frame video analysis of the 2020 Beirut explosion to derive the Landau-Whitham formula for overpressure layer thickness and demonstrate strong agreement between theoretical predictions and observational data.
This paper refutes the "all-or-nothing" expectation for local commuting charges in non-Hermitian bosonic chains by constructing explicit counterexamples with selective k-local charges, thereby demonstrating the limited universality of the Grabowski–Mathieu integrability test and providing a complete classification of such models.
This paper proposes a fully data-driven framework that combines adaptively computed, mechanism-aware precursors based on Optimal Time-Dependent (OTD) modes with a Transformer model to significantly extend the prediction horizons for extreme events in high-dimensional chaotic systems like Kolmogorov flow without requiring knowledge of the underlying governing equations.
This paper introduces a symmetry-resolved framework that decomposes high-dimensional Liouvillian dynamics into low-dimensional invariant sectors to systematically identify and characterize exceptional points in realistic open quantum systems, accompanied by a numerical diagnostic metric for quantifying their proximity.
This paper presents a mathematical model using replicator dynamics to demonstrate how social feedback mechanisms—specifically positive versus negative feedback—govern whether a society undergoes a discontinuous or continuous phase transition between widespread compliance and rule-breaking, thereby explaining the fragility of social order under weak institutions.
This study demonstrates that adaptive pendulum networks governed by Hebbian or STDP learning rules spontaneously generate diverse collective states, including a novel delay-free solitary state driven purely by phase-lag variations, and systematically maps these dynamical transitions using incoherence measures and stability analysis.
This paper investigates a pulse-coupled adaptive Winfree network with a frustration parameter, revealing that Hebbian adaptation spontaneously generates diverse collective states—including entrainment, bump, and chimera states without external forcing—and systematically characterizes these dynamics through new incoherence measures and analytical stability conditions.
This paper presents a framework for identifying and analyzing dynamics-induced active-inactive cluster patterns in complex networks that arise from symmetry breaking in systems with odd intrinsic dynamics and coupling, demonstrating that such patterns can exist without requiring network symmetries and providing a stability analysis based on coupling strength and intercluster weights.
This paper investigates the geometric, algebraic, and analytic properties of hyperelliptic functions as a generalization of Jacobi elliptic functions and demonstrates their role as potential solutions to the nonlinear Schrödinger and complex modified Korteweg-de Vries equations.
This paper extends the framework of invariant reduction for partial differential equations to handle geometric structures that are rescaled rather than strictly invariant by symmetries, establishing a shift rule that explains the emergence or loss of invariance in reduced systems and enabling the geometric construction of exact solutions without relying on integrability structures like Lax pairs.
This study investigates how inertial effects influence autotoxicity-mediated vegetation patterns on sloped arid terrains using a hyperbolic extension of the Klausmeier model, revealing that inertia acts as a destabilizing mechanism that enlarges the parameter range for uphill migrating bands, can induce hysteresis by reversing bifurcation regimes near onset, and increases pulse speeds in far-from-equilibrium conditions.
This paper investigates the existence and stability of Maxwell fronts—stationary interfaces between energetically equivalent states—in discrete nonlinear Schrödinger equations with competing nonlinearities (specifically quadratic-cubic and cubic-quintic terms), analyzing their persistence across weak and strong coupling regimes through linear stability and exponential asymptotic techniques.
This paper investigates the spectral and dynamical properties of the fractional nonlinear Schrödinger equation under harmonic confinement, revealing how the fractional order fundamentally reshapes the stability and evolution of stationary states and leads to distinct dynamical regimes in focusing and defocusing scenarios.
This paper introduces a computationally efficient method combining quantized local reduced-order modeling with adjoint-based optimization to successfully reconstruct trajectories and optimize spatiotemporally chaotic systems, achieving a 3.5-fold speedup over full-order models in a Kuramoto-Sivashinsky variational data assimilation problem.
This paper investigates three eigenfunction constraints on the differential-difference Kadomtsev-Petviashvili (DKP) hierarchy, demonstrating that a squared eigenfunction constraint yields the semi-discrete AKNS hierarchy while two distinct linear eigenfunction constraints lead to a combined semi-discrete Burgers hierarchy, with all results rigorously proved using recursive algebraic structures generated by master symmetries.
This paper demonstrates that the Poisson algebras of cohomological and -theoretic Coulomb branches in 3d necklace quiver gauge theories reproduce the equations of motion for rational and hyperbolic spin Ruijsenaars-Schneider models, respectively, while explicitly revealing their underlying affine Yangian and quantum toroidal superintegrability structures.
This study demonstrates that introducing competitive cyclic loops within active Potts models allows for the control of spatially coexisting states and pattern formation—ranging from simultaneous spiral waves to stochastic switching and homogeneous cycling—by tuning flip energies and the specific network topology of the state transitions.
This study demonstrates that symmetric reservoir topologies significantly enhance prediction accuracy for low-dimensional nonlinear dynamical systems with limited input dimensions, whereas high-dimensional chaotic systems like turbulent shear flow exhibit minimal sensitivity to such structural symmetries.
This paper establishes rigorous Lyapunov and port-Hamiltonian stability conditions for chaotic synchronization in a 5D memristive Hindmarsh-Rose neuron model and introduces a novel physics-informed neural network that successfully learns the underlying synchronization Hamiltonian while preserving its conservative and dissipative structures.