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Alfita P. Handayani

Publications and source records attributed to Alfita P. Handayani.

2 recordsLinked to original sources

An Open-Source Pseudo-Spectral Solver for Idealized Korteweg-de Vries Soliton Simulations

The Korteweg-de Vries (KdV) equation governs the propagation of nonlinear internal and surface gravity waves in shallow ocean environments, where the balance between nonlinear steepening and frequency-dependent dispersion produces solitons. This article presents sangkuriang, an open-source Python library that solves the KdV equation using Fourier pseudo-spectral spatial discretization and adaptive eighth-order Runge-Kutta time integration, accelerated via just-in-time (JIT) compilation. Validation across four progressively complex scenarios-isolated soliton propagation, symmetric interactions, overtaking collisions, and three-body interactions-demonstrates high-fidelity conservation of mass, momentum, and energy, with relative errors below $O(10^{-4})$. Measured soliton velocities agree with theoretical predictions within $5\%$, and complementary diagnostics based on spectral entropy and recurrence quantification analysis (RQA) confirm that computed solutions preserve the regular phase-space structure characteristic of integrable Hamiltonian systems. Running on a standard laptop, sangkuriang provides a robust, lightweight platform for reproducible numerical investigation of idealized nonlinear dispersive wave dynamics relevant to coastal and ocean engineering applications.

nlin.PS↗

dewi-kadita: A Python Library for Idealized Fish Schooling Simulation with Entropy-Based Diagnostics

Collective motion in fish schools exemplifies emergent self-organization in active matter systems, yet computational tools for simulating and analyzing these dynamics remain fragmented across research groups. We present dewi-kadita, an open-source Python library implementing the three-dimensional Couzin zone-based model with comprehensive entropy diagnostics tailored for marine collective behavior research. The library introduces seven information-theoretic metrics -- school cohesion entropy, polarization entropy, depth stratification entropy, angular momentum entropy, nearest-neighbor entropy, velocity correlation entropy, and school shape entropy -- that characterize distinct organizational features inaccessible to classical order parameters. These metrics combine into an Oceanic Schooling Index (OSI) providing a single scalar measure of collective disorder. Validation across four canonical configurations (swarm, torus, dynamic parallel, highly parallel) confirms correct reproduction of known phase behaviors: the swarm maintains disorder with polarization $P < 0.1$ and OSI $\approx 0.71$, while the highly parallel state achieves $P = 0.998$ with OSI $= 0.24$ and velocity correlation entropy vanishing to zero. The entropy framework successfully discriminates the torus and dynamic parallel configurations that exhibit comparable order parameter magnitudes through different organizational mechanisms. Numba just-in-time (JIT) compilation accelerates pairwise interaction calculations by $10$--$100\times$, enabling simulations of $150$--$250$ agents over $1000$--$2000$ time steps within five minutes on standard workstation hardware. NetCDF4 output ensures interoperability with oceanographic analysis tools. The library addresses the need for standardized, reproducible infrastructure in collective behavior modeling analogous to established molecular dynamics codes.

physics.comp-ph↗