Fast, Scalable, and Interactive Software for Landau-de Gennes Numerical Modeling of Nematic Topological Defects

Numerical modeling of nematic liquid crystals using the tensorial Landau-de Gennes (LdG) theory provides detailed insights into the structure and energetics of the enormous variety of possible topological defect configurations that may arise when the liquid crystal is in contact with colloidal inclusions or structured boundaries. However, these methods can be computationally expensive, making it challenging to predict (meta)stable configurations involving several colloidal particles, and they are often restricted to system sizes well below the experimental scale. Here we present an open-source software package that exploits the embarrassingly parallel structure of the lattice discretization of the LdG approach. Our implementation, combining CUDA/C++ and OpenMPI, allows users to accelerate simulations using both CPU and GPU resources in either single- or multiple-core configurations. We make use of an efficient minimization algorithm, the Fast Inertial Relaxation Engine (FIRE) method, that is well-suited to large-scale parallelization, requiring little additional memory or computational cost while offering performance competitive with other commonly used methods. In multi-core operation we are able to scale simulations up to supra-micron length scales of experimental relevance, and in single-core operation the simulation package includes a user-friendly GUI environment for rapid prototyping of interfacial features and the multifarious defect states they can promote. To demonstrate this software package, we examine in detail the competition between curvilinear disclinations and point-like hedgehog defects as size scale, material properties, and geometric features are varied. We also study the effects of an interface patterned with an array of topological point-defects.Comment: 16 pages, 6 figures, 1 youtube link. The full catastroph

Simulated Annealing for Topological Solitons

The search for solutions of field theories allowing for topological solitons requires that we find the field configuration with the lowest energy in a given sector of topological charge. The standard approach is based on the numerical solution of the static Euler-Lagrange differential equation following from the field energy. As an alternative, we propose to use a simulated annealing algorithm to minimize the energy functional directly. We have applied simulated annealing to several nonlinear classical field theories: the sine-Gordon model in one dimension, the baby Skyrme model in two dimensions and the nuclear Skyrme model in three dimensions. We describe in detail the implementation of the simulated annealing algorithm, present our results and get independent confirmation of the studies which have used standard minimization techniques.Comment: 31 pages, LaTeX, better quality pics at http://www.phy.umist.ac.uk/~weidig/Simulated_Annealing/, updated for publicatio

Topological solitons in highly anisotropic two dimensional ferromagnets

e study the solitons, stabilized by spin precession in a classical two--dimensional lattice model of Heisenberg ferromagnets with non-small easy--axis anisotropy. The properties of such solitons are treated both analytically using the continuous model including higher then second powers of magnetization gradients, and numerically for a discrete set of the spins on a square lattice. The dependence of the soliton energy $E$ on the number of spin deviations (bound magnons) $N$ is calculated. We have shown that the topological solitons are stable if the number $N$ exceeds some critical value $N_{\rm{cr}}$. For $N < N_{\rm{cr}}$ and the intermediate values of anisotropy constant $K_{\mathrm{eff}} <0.35J$ ($J$ is an exchange constant), the soliton properties are similar to those for continuous model; for example, soliton energy is increasing and the precession frequency $\omega (N)$ is decreasing monotonously with $N$ growth. For high enough anisotropy $K_{\mathrm{eff}} > 0.6 J$ we found some fundamentally new soliton features absent for continuous models incorporating even the higher powers of magnetization gradients. For high anisotropy, the dependence of soliton energy E(N) on the number of bound magnons become non-monotonic, with the minima at some "magic" numbers of bound magnons. Soliton frequency $\omega (N)$ have quite irregular behavior with step-like jumps and negative values of $\omega$ for some regions of $N$. Near these regions, stable static soliton states, stabilized by the lattice effects, exist.Comment: 17 page

Stable topological textures in a classical 2D Heisenberg model

We show that stable localized topological soliton textures (skyrmions) with $\pi_2$ topological charge $\nu \geq 1$ exist in a classical 2D Heisenberg model of a ferromagnet with uniaxial anisotropy. For this model the soliton exist only if the number of bound magnons exceeds some threshold value $N_{\rm cr}$ depending on $\nu$ and the effective anisotropy constant $K_{\rm eff}$. We define soliton phase diagram as the dependence of threshold energies and bound magnons number on anisotropy constant. The phase boundary lines are monotonous for both $\nu=1$ and $\nu >2$, while the solitons with $\nu=2$ reveal peculiar nonmonotonous behavior, determining the transition regime from low to high topological charges. In particular, the soliton energy per topological charge (topological energy density) achieves a minimum neither for $\nu=1$ nor high charges, but rather for intermediate values $\nu=2$ or $\nu=3$.Comment: 8 pages, 4 figure

On a continuation approach in Tikhonov regularization and its application in piecewise-constant parameter identification

We present a new approach to convexification of the Tikhonov regularization using a continuation method strategy. We embed the original minimization problem into a one-parameter family of minimization problems. Both the penalty term and the minimizer of the Tikhonov functional become dependent on a continuation parameter. In this way we can independently treat two main roles of the regularization term, which are stabilization of the ill-posed problem and introduction of the a priori knowledge. For zero continuation parameter we solve a relaxed regularization problem, which stabilizes the ill-posed problem in a weaker sense. The problem is recast to the original minimization by the continuation method and so the a priori knowledge is enforced. We apply this approach in the context of topology-to-shape geometry identification, where it allows to avoid the convergence of gradient-based methods to a local minima. We present illustrative results for magnetic induction tomography which is an example of PDE constrained inverse problem