Recent research on robotic fish mainly focused on the bionic structure design and realizing the movement with smart materials. Although many robotic fish have been proposed, most of these works were oriented toward shallow water environments and are mostly built with purely rigid structures, limiting the mobility and practical usability of robotic fish. Inspired by the stability of the real manta ray, a manta ray robot design is proposed with soft material made flapping wing based on an open-source ROV (Remotely Operated Vehicle). The flapping wing structure with three different materials mimics the wide pectoral fins of real manta rays, which have bones, muscles, and skin. Furthermore, its modular design makes it easy to install and disassemble. The kinematic and hydrodynamic analysis of the manta ray robot are simulated in this paper. The actual manta ray robot is fabricated and several sets of test are performed in the pool. The robot can swim forward continually and stably with a simple rolling and pitching pattern.

We develop a multitask and multifidelity Gaussian process (MMGP) model to accurately predict and optimize the multiobjective performance of a flapping foil while minimizing the cost of high-fidelity data. Through a comparison of three kernels, we have selected and applied the spectral mixture kernel and validated the robustness and effectiveness of a multiacquisition function. To effectively incorporate data with varying levels of fidelity, we have adopted a linear prior formula-based multifidelity framework. Additionally, Bayesian optimization with a multiacquisition function is adopted by the MMGP model to enable multitask active learning. The results unequivocally demonstrate that the MMGP model serves as a highly capable and efficient framework for effectively addressing the multiobjective challenges associated with flapping foils.

The mutual interaction among multiple fish during schooling has significant implication on motion pattern control and hydrodynamic optimization. However, the collective motion of multiple objects in a flow field forms a vast parameter space, causing difficulty in comprehensively analyzing and considering each parameter. To address this issue, the problem is simplified to a foil pair oscillating in a side-by-side configuration in a two-dimensional flow. Moreover, the Gaussian process regression predictive algorithm is combined with the fast and robust boundary data immersion method CFD algorithm to form a iteration loop for value prediction of the large parameter space. Through a relatively small number of simulations (around 1000 data points), we obtained predictions for the entire four-dimensional parameter space that consists of more than 160 000 parameter sets, greatly improving the computational efficiency. After obtaining the predicted space, we analyzed the interactions between different parameters and specially described the mechanism that gives rise to the unique effect of phase difference on the efficiency of the overall system and individual foils.

Physics-informed neural networks have emerged as a transformative framework for addressing operator learning and inverse problems involving the Korteweg–de Vries (KdV) equation for internal solitary waves. By integrating physical constraints with data-driven optimization, PINNs overcome the critical challenges of parameter unmeasurability in the KdV equation for internal solitary waves in two-layer fluid systems. This work addresses two problems: (1) operator learning constructs a mapping from parameters to solutions, enabling wave evolution predictions from unknown parameters. Comparative studies demonstrate prediction errors as low as 10−4 when using 1000 training points. (2) Inverse problem solving leverages sparse and potentially noisy observational data with physics-regularized constraints to invert nonlinear coefficients successfully. Compared to conventional approaches, this end-to-end differentiable paradigm unifies operator learning and inverse problem-solving while overcoming mesh discretization errors and high-dimensional parameter space iteration costs. The method shows effectiveness for internal wave problems in stratified fluids, providing both accurate forward modeling and robust parameter inversion capabilities, even under noise.