Shiyu Cheng

Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian et al.

Coordinating multiple autonomous agents to reach a target region while avoiding collisions and maintaining communication connectivity is a core problem in multi-agent systems. In practice, agents have a limited communication range. Thus, network links appear and disappear as agents move, making the topology state-dependent and time-varying. Existing distributed solutions to multi-agent reach-avoid problems typically assume a fixed communication topology, and thus are not applicable when encountering discontinuities raised by time-varying topologies. This paper presents a distributed optimization-based control framework that addresses these challenges through two complementary mechanisms. First, we introduce a truncation function that converts the time-varying communication graph into a smoothly state-dependent one, ensuring that constraints remain continuous as communication links are created or removed. Second, we employ auxiliary mismatch variables with two-time-scale dynamics to decouple globally coupled state-dependent constraints, yielding a singular perturbation system that each agent can solve using only local information and neighbor communication. Through singular perturbation analysis, we prove that the distributed controller guarantees collision avoidance, connectivity preservation, and convergence to the target region. We validate the proposed framework through numerical simulations involving multi-agent navigation with obstacles and time-varying communication topologies.

Guan Li, Yang Liu, Guihua Shan et al.

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. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

Yifei Jia, Shiyu Cheng, Yu Dong et al.

Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, and interpretation of temporal flow fields from scramjet combustion simulations. Our approach leverages hundreds of simulated combustion cases with varying initial conditions, each producing time-sequenced flow field images. We use pretrained Vision Transformers to extract high-dimensional embeddings from these frames, apply dimensionality reduction and density-based clustering to uncover latent combustion modes, and construct temporal trajectories in the embedding space to track the evolution of each simulation over time. To bridge the gap between latent representations and expert reasoning, domain specialists annotate representative cluster centroids with descriptive labels. These annotations are used as contextual prompts for a vision-language model, which generates natural-language summaries for individual frames and full simulation cases. The system also supports parameter-based filtering, similarity-based case retrieval, and coordinated multi-view exploration to facilitate in-depth analysis. We demonstrate the effectiveness of TemporalFlowViz through two expert-informed case studies and expert feedback, showing TemporalFlowViz enhances hypothesis generation, supports interpretable pattern discovery, and enhances knowledge discovery in large-scale scramjet combustion analysis.