ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging.

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces.

An In-Situ Visual Analytics Framework for Deep Neural Networks.

The past decade has witnessed the superior power of deep neural networks (DNNs) in applications across various domains. However, training a high-quality DNN remains a non-trivial task due to its massive number of parameters. Visualization has shown great potential in addressing this situation, as evidenced by numerous recent visualization works that aid in DNN training and interpretation.

Narrative In Situ Visual Analysis for Large-Scale Ocean Eddy Evolution.

The rapid development of high-performance computing systems has led to a rapid increase in the speed of flow field simulation calculations. However, large-scale simulation output data lead to storage bottlenecks and inefficient data analysis. In this work, we used in situ visualization to process the simulation analysis of large-scale flow fields.

STSRNet: Deep Joint Space-Time Super-Resolution for Vector Field Visualization.

We propose STSRNet, a joint space-time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution and high spatial resolution vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post hoc analysis.

CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics.

Convolutional neural networks (CNNs) have demonstrated extraordinarily good performance in many computer vision tasks. The increasing size of CNN models, however, prevents them from being widely deployed to devices with limited computational resources, e.g.

Efficient Volume Exploration Using the Gaussian Mixture Model.

The multidimensional transfer function is a flexible and effective tool for exploring volume data. However, designing an appropriate transfer function is a trial-and-error process and remains a challenge. In this paper, we propose a novel volume exploration scheme that explores volumetric structures in the feature space by modeling the space using the Gaussian mixture model (GMM).

Lifetimes of cagelike water clusters immersed in bulk liquid water: a molecular dynamics study on gas hydrate nucleation mechanisms.

Molecular dynamics simulations were performed to observe the evolution of cagelike water clusters immersed in bulk liquid water at 250 and 230 K. Totally, we considered four types of clusters--dodecahedral (5(12)) and tetrakaidecahedral (5(12)6(2)) cagelike water clusters filled with or without a methane molecule, respectively. The lifetimes of these clusters were calculated according to their Lindemann index (delta) using the criterion of delta> or =0.