This project introduces an interactive, web-based 3D data viewer specifically designed for the analysis of large-scale scientific cosmological datasets. The OpenCosmo Compute Portal [1], developed by Argonne National Laboratory’s Cosmological Physics and Advanced Computing group, provides easy access to cosmological simulations. Adding advanced visualization capabilities, such as those offered by this viewer, would significantly enhance the portal’s utility, allowing a broader audience to gain deeper insights into queried datasets without needing to build their own visualization tools.We address the critical need to democratize access to advanced scientific visualization, particularly for researchers who aren’t visualization experts. Leveraging modern web technologies, our viewer provides a fluid and responsive environment for exploring complex point cloud data. Key features include customizable visual properties, interactive selection, and robust state management, all accessible directly within a web browser, or virtual reality headset, if available.

Recent innovations in 3D reconstruction and the rise in popularity of Digital Twins present a unique opportunity for the integration of the two technologies for high quality data visualization. In this work, I present an architecture for integrating Gaussian Splat reconstructions with dynamic data and user interaction, implemented in Unreal Engine. I also explore the application of this method to digital twin creation and how it addresses specific visualization challenges.

We present a multi-GPU extension of the 3D Gaussian Splatting (3D-GS) pipeline for scientific visualization. Building on previous work that demonstrated high-fidelity isosurface reconstruction using Gaussian primitives, we incorporate a multi-GPU training backend adapted from Grendel-GS to enable scalable processing of large datasets. By distributing optimization across GPUs, our method improves training throughput and supports high-resolution reconstructions that exceed single-GPU capacity. In our experiments, the system achieves a 5.6× speedup on the Kingsnake dataset (4M Gaussians) using four GPUs compared to a single-GPU baseline, and successfully trains the Miranda dataset (18M Gaussians) that is an infeasible task on a single A100 GPU. This work lays the groundwork for integrating 3D-GS into HPC-based scientific workflows, enabling real-time post hoc and in situ visualization of complex simulations.

General Experimentation and Natural Interface Utility System (GENIUS), is an AI personal assistant specifically tailored for scientists engaged in experimentation and research. GENIUS integrates Large Language Models (LLM), with immersive mixed reality (XR) capabilities. Through natural speech recognition, users can interact effortlessly with GENIUS to ask questions, visualize and manipulate complex 3D models, and execute computational jobs on Argonne Leadership Computing Facility (ALCF) supercomputers.

Mixed reality (MR) enables immersive, intuitive engagement with scientific data. When paired with AI-driven assistants, it has the potential to transform traditional workflows. In this paper, we introduce a modular agentic architecture for scientific visualization in MR, designed to balance general-purpose flexibility with domain-specific extensibility. Our modular architecture supports composable tools, contextual reasoning, and dynamic task execution. We outline a three-layer design, domain module integration, and orchestration of multistep workflows. We demonstrate the system’s capabilities through use cases in biology and general-purpose scientific visualization, including protein interaction networks and remote ParaView-based rendering. The result is a flexible and extensible foundation for spatial scientific computing.

Lagrangian representations of flow fields have gained prominence for enabling fast, accurate analysis and exploration of time-varying flow behaviors. In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian representations and demonstrated accurate particle tracing for an analytic 2D flow field. In this paper, we extend and build upon this prior work in significant ways. First, we evaluate the performance of DNN models to accurately trace particles in various settings, including 2D and 3D time-varying flow fields, flow fields from multiple applications, flow fields with varying complexity, as well as structured and unstructured input data. Second, we conduct an empirical study to inform best practices with respect to particle tracing model architectures, activation functions, and training data structures. Third, we conduct a comparative evaluation of prior techniques that employ flow maps as input for exploratory flow visualization. Specifically, we compare our extended model against its predecessor by Han et al. (2021), as well as the conventional approach that uses triangulation and Barycentric coordinate interpolation. Finally, we consider the integration and adaptation of our particle tracing model with different viewers. We provide an interactive web-based visualization interface by leveraging the efficiencies of our framework, and perform high-fidelity interactive visualization by integrating it with an OSPRay-based viewer. Overall, our experiments demonstrate that using a trained DNN model to predict new particle trajectories requires a low memory footprint and results in rapid inference. Following best practices for large 3D datasets, our deep learning approach using GPUs for inference is shown to require approximately 46 times less memory while being more than 400 times faster than the conventional methods.

This article presents a comprehensive study of rendering techniques for 3D line sets with transparency. The rendering of transparent lines is widely used for visualizing trajectories of tracer particles in flow fields. Transparency is then used to fade out lines deemed unimportant, based on, for instance, geometric properties or attributes defined along with them. Accurate blending of transparent lines requires rendering the lines in back-to-front or front-to-back order, yet enforcing this order for space-filling 3D line sets with extremely high-depth complexity becomes challenging. In this article, we study CPU and GPU rendering techniques for transparent 3D line sets. We compare accurate and approximate techniques using optimized implementations and several benchmark data sets. We discuss the effects of data size and transparency on quality, performance, and memory consumption. Based on our study, we propose two improvements to per-pixel fragment lists and multi-layer alpha blending. The first improves the rendering speed via an improved GPU sorting operation, and the second improves rendering quality via transparency-based bucketing.

We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.