Bayesian Uncertainty Identification of Model Parameters for the Jointed Structures with Nonlinearity

Jointed structures in engineering naturally perform with some of nonlinearity and uncertainty, which significantly affect the dynamic characteristics of the structural system. In this paper, the method of Bayesian uncertainty identification of model parameters for the jointed structures with local nonlinearity is proposed. Firstly, the nonlinear stiffness and damping of the joints under the random excitation are represented with functions of excitation magnitude in terms of the equivalent linearization. The process of uncertainty identification is separated from the representation of local nonlinearity. In this way, the dynamic behavior of the joints is penetratingly characterized instead of ascribing the nonlinearity to uncertainty. Secondly, a variable-expanded Bayesian (VEB) method is originally proposed to identify the mixed of aleatory and epistemic uncertainties of model parameters. Different from traditional Bayesian identification, the aleatory uncertainties of model parameters are identified as one of the most important parts rather than only measurement noise of output. Notablely, a series of intermediate variables are introduced to expand the parameter with aleatory uncertainty in order to overcome the difficulty of establishing the likelihood function. Moreover, a 3-DOF numerical example is illustrated with case studies to verify the proposed method. The influence of observed sample size and prior distribution selection on the identification results is tested. Furthermore, an engineering example of the jointed structure with rubber isolators is performed to show the practicability of the proposed method. It is indicated that the computational model updated with the accurately identified parameters with both nonlinearity and uncertainty has shown the excellent predictive capability.

Local nonlinearity engineering of evanescent-field-interaction fiber devices embedding in black phosphorus quantum dots

Tapered fiber (TF) and D-shaped fiber (DF) are two types of widely investigated devices in facilitating evanescent-field interactions with external materials. Although they have been found to be particularly useful in various ultrafast regimes, to date there is still no clear or systematic investigation on their local nonlinearities as well as the exerted influences on ultrafast behaviors. Herein, we present such thorough investigation through local nonlinearity engineering on TF and then in contrast with a DF as a reference. Optically deposited black phosphorus quantum dots (BPQDs) are used for saturable absorption. The nanometer-scale extremely small sizes of the BPQDs helpfully eliminate size-induced uncertainties or distortions during both device fabrication and the latter light–matter interaction. For the TF, in the experiment, it is found that the local nonlinear effect starts to be rather appreciable as the TF shrinks to a moderate thickness. Remarkably in comparison, the local nonlinearity of the DF itself can even be neglected reasonably, but after coating with BPQDs, it possesses a much larger modulation depth than any of the used BPQDs-coated TFs with different thicknesses/lengths. Further, we theoretically analyze the related locally nonlinear effects and reveal, for the first time, the direct origin of saturable absorption with evanescent-field-based general structures.

Local quasi-linear embedding based on kronecker product expansion of vectors

Locally Linear Embedding (LLE) is honored as the first algorithm of manifold learning. Generally speaking, the relation between a data and its nearest neighbors is nonlinear and LLE only extracts its linear part. Therefore, local nonlinear embedding is an important direction of improvement to LLE. However, any attempt in this direction may lead to a significant increase in computational complexity. In this paper, a novel algorithm called local quasi-linear embedding (LQLE) is proposed. In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods.

Nonlinear tuning of PT symmetry and non-Hermitian topological states

Topology, parity-time (PT) symmetry, and nonlinearity are at the origin of many fundamental phenomena in complex systems across the natural sciences, but their mutual interplay remains unexplored. We established a nonlinear non-Hermitian topological platform for active tuning of PT symmetry and topological states. We found that the loss in a topological defect potential in a non-Hermitian photonic lattice can be tuned solely by nonlinearity, enabling the transition between PT-symmetric and non–PT-symmetric regimes and the maneuvering of topological zero modes. The interaction between two apparently antagonistic effects is revealed: the sensitivity close to exceptional points and the robustness of non-Hermitian topological states. Our scheme using single-channel control of global PT symmetry and topology via local nonlinearity may provide opportunities for unconventional light manipulation and device applications.