arXiv subjects

Jinge Wang

Publications and source records attributed to Jinge Wang.

7 recordsLinked to original sources

arXiv2026

A semi-analytical pseudo-spectral method for 3D Boussinesq equations of rotating, stratified flows in unbounded cylindrical domains

We present a pseudo-spectral method for solving the three-dimensional Boussinesq equations in unbounded cylindrical domains, specifically tailored for rotating, stably stratified flows subject to strong azimuthal shear. To effectively capture the global geometry without sacrificing spectral accuracy, the spatial discretization employs Fourier expansions in the azimuthal and axial directions alongside mapped associated Legendre polynomials in the radial direction. This basis spans the semi-infinite domain while analytically resolving the coordinate singularity at the origin. While this spectral framework ensures high spatial fidelity, the temporal integration of these rotating shear flows presents a formidable computational challenge due to the numerical stiffness driven by fast restorative wave forces and rapid background advection. To circumvent this, we develop an exponential time differencing (ETD) scheme that analytically integrates the fully coupled linear operator, including the radially dependent advective cross terms. By encoding the physical resonance characteristics and stability limits of the background flow directly into the integration operators, the proposed ETD formulation removes the numerical stability constraints imposed by the background shear and stratification. This permits integration time steps scaled by the slow macroscopic evolution of the physical instabilities rather than the fast background kinematics, offering significant performance gains over standard mixed implicit-explicit schemes. The method's accuracy and stability are validated through the precise conservation of energy and angular momentum, establishing a robust framework for simulating instabilities in astrophysical and geophysical vortices.

physics.flu-dyn↗

arXiv2024

Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas

Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments indicates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.

cs.AI↗

arXiv2024

Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review

The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines. We surveyed the applications of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

q-bio.OT↗

arXiv2024

Perturbation analysis of triadic resonance in columnar vortices: selection rules and the roles of external forcing and critical layers

The remarkable robustness of columnar vortices suggests the existence of fundamental constraints that prevent spontaneous disintegration. In this work, we investigate the weakly nonlinear stability of such flows, demonstrating that the triadic resonance of wave modes is governed by a set of hydrodynamic ``selection rules''. By employing a multi-scale perturbation analysis, we prove that resonant interactions between smooth neutral modes, specifically regular Kelvin waves and discrete critical layer modes with passive singularities, are strictly conservative and confined to the Manley--Rowe relations. Using wave pseudoenergy within a large-$k$ WKBJ framework, we show that these rules topologically prohibit intrinsic instability, analogous to the forbidden transitions of quantum mechanics. Consequently, the breakdown of a columnar vortex requires a specific symmetry-breaking mechanism to overcome this barrier. We identify two distinct pathways: (1) \textit{Parametric instability}, a limiting case where one mode is maintained externally. By generalising beyond the specific spatial and temporal assumptions of classical studies (e.g., elliptical instability) and leveraging a tuning method based on non-degenerate perturbation theory, our framework admits arbitrary driving frequencies and identifies new instability configurations involving discrete critical layer modes. (2) \textit{Active critical layers}, where an embedded wave-mean resonance enables the direct, non-conservative extraction of mean-flow energy. These findings provide theoretical guidance for flow control, suggesting that aircraft wake vortex mitigation requires either tuned external forcing or the excitation of critical layers (e.g., via thermal stratification) to trigger the forbidden transitions.

physics.flu-dyn↗

arXiv2022

Airfoil Optimization using Design-by-Morphing

We present Design-by-Morphing (DbM), a novel design methodology applicable to creating a search space for topology optimization of 2D airfoils. Most design techniques impose geometric constraints and sometimes designers' bias on the design space itself, thus restricting the novelty of the designs created, and only allowing for small local changes. We show that DbM methodology does not impose any such restrictions on the design space and allows for extrapolation from the search space, thus granting truly radical and large search space with a few design parameters. In comparison to other shape design methodologies, we apply DbM to create a search space for 2D airfoils. We optimize this airfoil shape design space for maximizing the lift-over-drag ratio, $CLD_{max}$, and stall angle tolerance, $\Delta \alpha$. Using a bi-objective genetic algorithm to optimize the DbM space, it is found that we create a Pareto-front of radical airfoils exhibiting remarkable properties for both objectives.

math.GT↗

arXiv2020

Deformable Kernel Convolutional Network for Video Extreme Super-Resolution

Video super-resolution, which attempts to reconstruct high-resolution video frames from their corresponding low-resolution versions, has received increasingly more attention in recent years. Most existing approaches opt to use deformable convolution to temporally align neighboring frames and apply traditional spatial attention mechanism (convolution based) to enhance reconstructed features. However, such spatial-only strategies cannot fully utilize temporal dependency among video frames. In this paper, we propose a novel deep learning based VSR algorithm, named Deformable Kernel Spatial Attention Network (DKSAN). Thanks to newly designed Deformable Kernel Convolution Alignment (DKC_Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can better exploit both spatial and temporal redundancies to facilitate the information propagation across different layers. We have tested DKSAN on AIM2020 Video Extreme Super-Resolution Challenge to super-resolve videos with a scale factor as large as 16. Experimental results demonstrate that our proposed DKSAN can achieve both better subjective and objective performance compared with the existing state-of-the-art EDVR on Vid3oC and IntVID datasets.

cs.CV↗

arXiv2020

AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results

This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the low-resolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time.

cs.CV↗