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3D Gaussian Splatting (3DGS) supports fast training and real-time rendering, but its optimization process remains difficult to inter- pret. Existing viewers mainly expose the final reconstructed scene and offer limited support for explaining how Gaussian properties contribute to visible artifacts or evolve during training. We present Vis4GS, a multi-view visual analytics tool for primitive-level diag- nosis of 3DGS reconstruction artifacts. Built on the original 3DGS viewer and training framework, Vis4GS links rendered artifacts to Gaussian properties, View Coverage, training progress, and Gaus- sian genealogy through four linked views: an interactive Gaussian analysis view, a property timeline view, a Gaussian densification tree view, and a log and control panel. The system supports Gaus- sian selection, blur and needle-like artifact scoring, View Cover- age analysis, and multiscale genealogy exploration of clone, split, prune, and clone-split events. By connecting scene-level artifacts with primitive-level evidence and optimization history, Vis4GS en- ables a structured workflow for diagnosing reconstruction failures beyond final-image inspection and global metrics. A user study also shows that Vis4GS provides stronger support for usability and artifact understanding than the original 3DGS viewer.

Convective heat transfer is traditionally visualized from a Eulerian perspective using scalar temperature fields, offering limited insight into the underlying transport mechanisms. A Lagrangian view, analogous to mass transport along fluid paths, can reveal coherent structures and transport routes invisible from a Eulerian view of temperature. However, heat transport is aperiodic and non-conservative, hampering the application of fluid mixing and transport visualization techniques, developed primarily for time-periodic, conservative transport. We present a particle-based visualization technique that addresses these challenges by advecting massless particles along a time-reparameterized spacetime formulation of thermal transport, accumulating path contributions to reveal coherent transport routes and finite-time attracting and repelling structures that conventional methods cannot show.

We present Streami, an extensible GPU-accelerated library for the computation of field lines in fluid flows on high-performance computers. Streami acts as a thin layer used for both post-hoc or in-situ analysis and can interface with existing MPI applications. We discuss Streami’s application programming interface, key design decisions that led to Streami’s high performance and extensibility, as well as extensions to support different fluid flow field representations. We also present a sample application for rapid prototyping and interactive seed point placement. Streami is released under a permissive open-source software license.

Implicit neural representation (INR) methods provide continuous coordinate-to-value mappings and integrate naturally with direct volume rendering, making them attractive for representing volumetric data. However, existing INR-based approaches for volumetric data are inherently lossy, and even small reconstruction errors can propagate through rendering and downstream analysis. In this work, we explore Lossless-INR, a lossless INR framework for 3D scientific volumetric data based on bit-plane decomposition. By decomposing each voxel value into binary bit-planes, we reformulate reconstruction as per-bit binary classification, so that exact recovery reduces to predicting every bit correctly. To make this optimization tractable while keeping the representation compact, we combine an octree block-partitioning strategy that adaptively subdivides complex regions with a ternary feature-grid network whose grid entries are parameterized by a ternary set of values. Experiments on diverse volumetric datasets show that this design can achieve zero bit-error rate and bit-exact reconstruction, enabling faithful rendering and downstream analysis with a compact representation. The code is available at https://github.com/TouKaienn/Lossless-INR.

Automatic evaluation of visualization quality enables optimization of visualization parameters without human intervention. In isosurface visualization, prior work estimates quality by reconstructing a 3D surface from multiple images using NeRF, followed by volumetric reconstruction and comparison to the original data. In this paper, we replace the NeRF-based multi-view reconstruction with a diffusion-based single-image 3D reconstruction model. This formulation removes the need for auxiliary views and aligns the evaluation setting with single-image visualization scenarios. Because the reconstructed geometry is not produced in the original coordinate system, we introduce an explicit alignment step to resolve global pose and scale ambiguities. Experimental results on benchmark datasets show that the proposed approach reduces surface reconstruction error and improves runtime relative to the NeRF-based pipeline. These results suggest that strong learned priors provide a more reliable reconstruction backend for reconstruction-based evaluation while enabling more efficient workflows.

Topological analysis of scalar fields yields structures such as the Morse–Smale complex (MSC) that summarize salient features across multiple scales. Existing MSC extraction algorithms typically assume an explicit representation of the input field, such as a discretely sampled mesh. However, recent advances in visualization have popularized implicit field representations, for which these assumptions no longer hold. In this work, we address the problem of extracting an MSC from an implicitly defined 2D scalar field. We present a method for constructing a triangulated piecewise-linear (PL) mesh that aims to preserve the critical points of an underlying implicit scalar field. Our central insight is that if all edges are monotonic with respect to the underlying field, then the resulting PL approximation is topologically consistent with respect to critical points. Based on this insight, we introduce a refinement procedure that mitigates monotonicity violations. Requiring only pointwise evaluations and modest mesh refinement, the approach produces PL meshes that are correct with regards to critical points in our experiments. Finally, we demonstrate that additional targeted refinement improves the geometric fidelity of MSC separatrices.

Recent advances in AI for Science have enabled natural language (NL) interfaces for scientific data analysis. In turbomachinery CFD post-processing, translating ambiguous high-level analytical goals (e.g., vortex identification) into precise visualization procedures supporting complex domain-specific analysis is challenging. We present CATVis, Collaborative multi-agent workflow system that bridges this gap by transforming NL intents into structured middle representation for visualization. Our approach reformulates domain-specific visualization procedures as composable workflow representations, and use multi agent to generate workflow representations via intent planning, template generation, and error-aware refinement, where each stage incrementally updates a shared structured representation. We evaluate the impact of external knowledge and workflow structuring on generation accuracy, demonstrating that the proposed approach significantly improves complex workflow generation correctness while reducing prompt complexity.

Designing safe and sustainable chemicals is critical to combat chemical pollution in our environment. Computational and AI-assisted methods have been developed to aid de novo molecule design. However, data on the environmental impacts of chemical compounds are sparse, resulting in low-fidelity machine learning (ML) oracles and unreliable candidate proposals. Furthermore, many automated molecular design approaches rely on numerical scoring functions that cannot fully capture the nuanced chemical intuition of expert scientists required for real-world molecular design. Instead, we present GEMS—an interactive visual analytics tool for human-in-the-loop molecular optimization that lets domain experts directly collaborate with an evolutionary genetic algorithm. Users continuously guide the search using domain knowledge through high-level, parametric modification of the scoring function alongside direct, granular control over molecule populations. GEMS requires no programming or expertise in ML or evolutionary optimization. A usage scenario demonstrates its application in designing sustainable antioxidant alternatives, and interviews with domain scientists provide feedback on its usefulness.