When electrons tunnel through a barrier formed by the strong laser field and the two-center potential of a diatomic molecule, a double-slit-like interference can occur. However, this interference effect can not be probed directly right now, as it is strongly coupled with other dynamical processes during tunneling. Here, we show numerically and analytically that orthogonally polarized two-color (OTC) laser fields are capable of resolving the interference effect in tunneling, while leaving clear footprints of this effect in photoelectron momentum distributions. Moreover, this effect can be manipulated by changing the relative field strength of OTC fields.

In this work, we proposed a sensitivity-enhanced temperature sensor, a compact harmonic Vernier sensor based on an in-fiber Fabry–Perot Interferometer (FPI), with three reflective interfaces for the measurement of gas temperature and pressure. FPI consists of air and silica cavities formulated by single-mode optical fiber (SMF) and several short hollow core fiber segments. One of the cavity lengths is deliberately made larger to excite several harmonics of the Vernier effect that have different sensitivity magnifications to the gas pressure and temperature. The spectral curve could be demodulated using a digital bandpass filter to extract the interference spectrum according to the spatial frequencies of resonance cavities. The findings indicate that the material and structural properties of the resonance cavities have an impact on the respective temperature sensitivity and pressure sensitivity. The measured pressure sensitivity and temperature sensitivity of the proposed sensor are 114 nm/MPa and 176 pm/°C, respectively. Therefore, the proposed sensor combines ease of fabrication and high sensitivity, making it great potential for practical sensing measurements.

Transition dynamics have been studied widely in two-mode Bose–Einstein condensate systems with linear coupling effects. Nonlinear interaction between atoms would induce a nonlinear Josephson oscillation, which admits an oscillation form different from the classical Josephson oscillation. Similar extensions for the Rosen–Zener transition and the Landau–Zener transition also suggest that a strong nonlinear strength would induce nonlinear-type transition dynamics, which is in sharp contrast to the linear process. Interestingly, here we show a standard Josephson oscillation that has a constant linear coupling strength, no matter how great the nonlinear interaction strength is. This characteristic is in sharp contrast to the nonlinear Josephson oscillation reported before in nonlinear coupled systems. By manipulating the linear coupling strength with the RF field, we demonstrate that the Rosen–Zener transition particles, with an invariant distribution profile, can be managed well under exponential and periodic forms. The nonlinear interaction strength is found to have no effect on the transition rate between atoms in the two components for the case where the inter- and intra-atomic interaction strengths are equal. Furthermore, we test the robustness of the transition dynamics against parameter deviations and noises in the initial condition. These results are helpful for controllable quantum state preparations.

The real-time surface thermal monitoring of thermal protection structures (TPSs) is crucial for hypersonic vehicle safety. This study proposes an effective approach for real-time temperature reconstruction by integrating embedded sensor arrays with an enhanced full-time domain inversion algorithm, utilizing the overlapping sliding window method. An array of three evenly spaced sensors is used for TPS monitoring. Notably, the inversion approach eliminates the need for prior knowledge of the TPS’s thermal parameters. It exhibits remarkable practicality with low-frequency sampling requirements (1 Hz) and robust noise resistance. Through numerical simulations and a quartz lamp side heating experiment, it is demonstrated that the window size and data noise have great influence on the temperature reconstruction accuracy, but the window slip step has little influence. The mean relative error of the inversion temperature decreases exponentially as the window size increases, and the optimal window duration is equal to the thermal hysteresis time. The study investigates the impact of three noise filtering methods on the inversion accuracy, finding that the Savitzky-Golay filtering significantly enhances measurement precision, reducing mean relative error from 18.4% to 6.7%. These results highlight the potential of the proposed real-time sensor method for practical engineering applications, offering a reliable and efficient solution for real-time TPS temperature monitoring.

Soft- and hard-constrained Physics Informed Neural Networks (PINNs) have achieved great success in solving partial differential equations (PDEs). However, these methods still face great challenges when solving the Navier-Stokes equations (NSEs) with complex boundary conditions. To address these challenges, this paper introduces a novel complementary scheme combining soft and hard constraint PINN methods. The soft-constrained part is thus formulated to obtain the preliminary results with a lighter training burden, upon which refined results are then achieved using a more sophisticated hard-constrained mechanism with a primary network and a distance metric network. Specifically, the soft-constrained part focuses on boundary points, while the primary network emphasizes inner domain points, primarily through PDE loss. Additionally, the novel distance metric network is proposed to predict the power function of the distance from a point to the boundaries, which serves as the weighting factor for the first two components. This approach ensures accurate predictions for both boundary and inner domain areas. The effectiveness of the proposed method on the NSEs problem with complex boundary conditions is demonstrated by solving a 2D cylinder wake problem and a 2D blocked cavity flow with a segmented inlet problem, achieving significantly higher accuracy compared to traditional soft- and hard-constrained PINN approaches. Given PINN's inherent advantages in solving the inverse and the large-scale problems, which are challenging for traditional computational fluid dynamics (CFD) methods, this approach holds promise for the inverse design of required flow fields by specifically-designed boundary conditions and the reconstruction of large-scale flow fields by adding a limited number of training input points. The code for our approach will be made publicly available.