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Arjun R

Publications and source records attributed to Arjun R.

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Nonreciprocal McKean-Vlasov Equations: From Stationary Instabilities to Travelling Waves

Nonreciprocal interactions, in which action-reaction symmetry is broken, provide a powerful route to collective dynamics that cannot be captured by equilibrium free-energy minimisation. Here, we introduce and analyse a two-species nonreciprocal McKean-Vlasov equation derived from an underlying system of interacting stochastic particles. Combining linear stability analysis, weakly nonlinear arguments, pseudo-spectral simulations, and Langevin particle dynamics, we show that the structure of nonreciprocity controls the onset and nature of collective order. For spatially uniform weak nonreciprocity, asymmetry shifts the critical diffusion threshold but produces only stationary instabilities, indicating that uniform imbalance alone is insufficient to generate sustained time-dependent motion. In contrast, spatially modulated nonreciprocity fundamentally enriches the dynamics: depending on its symmetry and coupling to the interaction potential, the homogeneous state can lose stability through Hopf bifurcations, giving rise to standing and travelling wave states. We identify both subcritical and supercritical Hopf transitions, relate the selected patterns to Landau saturation coefficients, and show that travelling waves can emerge even in the weak-nonreciprocity regime without explicit microscopic run-and-chase rules. Direct Langevin simulations confirm that these oscillatory and travelling states persist at the particle level and are not artefacts of the continuum mean-field description. Our results establish nonreciprocal McKean-Vlasov equations as a minimal framework for understanding how spatially structured asymmetric interactions generate self-organized motion, dynamical phase transitions, and nonequilibrium collective order.

cond-mat.stat-mech

Nonreciprocal Blume-Capel Model with Antisymmetric Single-Ion Anisotropies

We investigate the interplay between nonreciprocal interactions and chemical-potential imbalance in a two-species nonreciprocal Blume-Capel model. Combining a systematic mean-field bifurcation analysis with large-scale Monte Carlo simulations in two and three dimensions, we map the model's dynamical regimes and transitions. Mean-field theory predicts a rich phase structure -- disorder, a time-dependent 'swap' (limit-cycle) phase, and static ordered states -- separated by Hopf, saddle-node on invariant circle, saddle-node of limit cycles, pitchfork and saddle-node bifurcations. In two dimensions, Monte Carlo simulations reveal that spiral defects destabilise global swapping and, unless vacancies are strongly favoured, destroy long-range order. Crucially, a finite single-ion anisotropy $\Delta_\alpha = - \Delta_\beta$ promotes vacancy occupation in the $\alpha$ species and suppresses nonreciprocal dynamics, thereby restoring a robust static ordered phase. Finite-size scaling of susceptibility and Binder cumulants places the disorder to static transition firmly in the 2D Ising universality class. Moreover, within the static ordered phase, we observe a crossover that sharpens into a line of first-order phase transitions; these two regimes are separated by a critical point, analogous to the termination of the liquid-gas coexistence curve. In three dimensions, simulations largely mirror mean-field expectations, though swap to static ordering occurs indirectly via a disordered regime. Our results demonstrate that vacancy energetics provide a simple, experimentally relevant control knob that stabilises equilibrium-like order in nonreciprocal systems and that defects can generate novel critical behaviour.

cond-mat.stat-mech

Kinetic Random-Field Nonreciprocal Ising Model

We introduce and analyse the kinetic random-field nonreciprocal Ising model, which incorporates bimodal (double-delta) diffusive disorder along with pairwise nonreciprocal interactions between two different species. Using mean-field and effective-field theory, in combination with kinetic Monte Carlo simulations (3D Glauber dynamics), we identify a nonequilibrium tricritical (Bautin) point separating Hopf-type transitions (continuous) from saddle-node-of-limit-cycle (SNLC) transitions (discontinuous). For a weak random field which is less than a critical value, the onset of collective oscillations (the "swap" phase) occurs via a supercritical Hopf bifurcation, whereas for fields greater than the critical value, the transition is first-order (SNLC), exhibiting hysteresis and Binder-cumulant signatures. The finite-size scaling of the susceptibility is consistent with the distinct critical and discontinuous behaviour shown in the Hopf and SNLC regimes, respectively (effective exponents $\approx1.96$ in the Hopf regime and $\approx3.0$ in the SNLC regime). Additionally, in the first-order regime, the swap phase is sustained only above a threshold nonreciprocity, and this threshold increases monotonically with the disorder strength. We further identify a new droplet-induced swap phase in the larger field-strength region, which cycles eight different metastable states. A dynamical free-energy picture rationalises droplet nucleation as the mechanism for these cyclic jumps. Together, these results demonstrate how disorder and nonreciprocity combined generate rich nonequilibrium criticality, with implications for driven and active systems.

cond-mat.stat-mech

Predictive modeling of stock indices closing from web search trends

The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding index close values from different countries stock exchanges are collected and analyzed. Empirical findings show global stock price search interests correlates more with developing economies with fewer effects in south asian stock exchanges apart from strong influence in western countries. Finally this study calls for development in expert decision support systems with the synthesis of using big data sources on forecasting market outcomes

q-fin.GN