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Magnetic monopoles, despite their ongoing experimental search as elementary particles, have inspired the discovery of analogous excitations in condensed matter systems. In chiral condensed matter systems, emergent monopoles are responsible for the onset of transitions between topologically distinct states and phases, such as in the case of transitions from helical and conical phase to A-phase comprising periodic arrays of skyrmions. By combining numerical modeling and optical characterizations, we describe how different geometrical configurations of skyrmions terminating at monopoles can be realized in liquid crystals and liquid crystal ferromagnets. We demonstrate how these complex structures can be effectively manipulated by external magnetic and electric fields. Furthermore, we discuss how our findings may hint at similar dynamics in other physical systems and their potential applications. 

Interest in soft robotics has increased in recent years due to their potential in a myriad of applications. A wide variety of soft robots has emerged, including bio-inspired robotic swimmers such as jellyfish, rays, and robotic fish. However, the highly nonlinear fluid-structure interactions pose considerable challenges in the analysis, modeling, and feedback control of these soft robotic swimmers. In particular, developing models that are of high fidelity but are also amenable to control for such robots remains an open problem. In this work, we pro- pose a data-driven approach that exploits Koopman operators to obtain a linear representation of the soft swimmer dynamics. Specifically, two methodologies are explored for obtaining the basis functions of the the operator, one based on data-based derivatives estimated using high-gain observers, and the other based on the dynamics structure of a tail-actuated rigid-body robotic fish. The resulting approximate finite-dimensional operators are trained and evaluated using data from high-fidelity CFD simulations that incorporate fluid-structure interactions. Validation results demonstrate that, while both methods are promising in producing control-oriented models, the approach based on derivative estimates shows higher accuracy in state prediction.