Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user systems. Novel view synthesis techniques can address this by producing a small, interactive model of the data, requiring only a set of training images to learn from. While these models allow accessible visualization of large data and complex scenes, they do not provide the interactions needed for scientific volumes, as they do not support interactive selection of transfer functions and lighting parameters. To address this, we introduce Volume Encoding Gaussians (VEG), a 3D Gaussian-based representation for volume visualization that supports arbitrary color and opacity mappings. Unlike prior 3D Gaussian Splatting (3DGS) methods that store color and opacity for each Gaussian, VEG decouple the visual appearance from the data representation by encoding only scalar values, enabling transfer function-agnostic rendering of 3DGS models. To ensure complete scalar field coverage, we introduce an opacity-guided training strategy, using differentiable rendering with multiple transfer functions to optimize our data representation. This allows VEG to preserve fine features across a dataset’s full scalar range while remaining independent of any specific transfer function. Across a diverse set of volume datasets, we demonstrate that our method outperforms the state-of-the-art on transfer functions unseen during training, while requiring a fraction of the memory and training time.<br><br>
Index Terms - Volume Rendering, 3D Gaussian Splatting

Gaussian density comparison of VEG models trained using either a linear opacity map, an inverse linear opacity map, or our multiple opacity map method using three steps on the RBL dataset.

Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering

In situations where it is difficult to perform robotic laboratory tasks with physical hardware - such as handling radioactive materials, or collecting imitation learning demonstrations at scale - it is useful to be able to simulate the robotic laboratory environment.  Mixed Reality interfaces enable operators to interact with these simulations in an intuitive and immersive format. In order to achieve quality, accurate control from the operator, it is important that the simulated hardware, environment, and interaction paradigms are as good as they can be. In this work, we perform a survey of existing techniques for robot simulation, robot control, 3D scene reconstruction, and VR user interaction, and propose methodology for examining the most effective combination of methods for remote teleoperation. Further, we examine how to address user-perceived differences between the simulation interface and the physical control interface.

The goal is to create a VR-enabled User Interface that communicates with a robot controller to provide a common method for interacting with remote and simulated robotic laboratories.

Using Virtual Reality for Remote Dexterous Manipulation: An Exploration of Methods

The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared network links. Parallel discrete event simulation (PDES) is commonly used to analyze workload interference. However, high-fidelity PDES is computationally expensive, making it impractical for large-scale or real-time scenarios. Hybrid simulation that incorporates data-driven surrogate models offers a promising alternative, especially for forecasting application runtime, a task complicated by the dynamic behavior of network traffic. We present \ourmodel, a surrogate model that combines graph neural networks (GNNs) and large language models (LLMs) to capture both spatial and temporal patterns from port level router data. \ourmodel outperforms existing statistical and machine learning baselines, enabling accurate runtime prediction and supporting efficient hybrid simulation of Dragonfly networks.

Publications - 2026

Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering

Using Virtual Reality for Remote Dexterous Manipulation: An Exploration of Methods

SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems

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