To study complex real-world phenomena using computer simulations, scientists often rely on ensemble datasets generated from multiple simulation runs with varying parameter configurations. This process can produce ensemble datasets with many members, making traditional data analysis pipelines impractical due to limited I/O bandwidth and disk capacity. Distribution-based data representations have been proposed as a promising solution. Processing data in situ to generate compact distribution-based representations not only alleviates the challenges of limited I/O bandwidth and disk capacity but also enables uncertainty quantification, thus mitigating the risk of misinterpretation. Nevertheless, distribution-based methods inherently sacrifice spatial information of data samples within the distribution, potentially reducing precision in the data analysis pipeline. To address this issue, we introduce a deep learning model to reconstruct data volume from the distribution representation. Instead of using a model that predicts a data block directly from its distribution representation, we propose a deep learning model based on the Sinkhorn operator and Gumbel trick that learns to map samples drawn from a distribution to spatial locations within the block. The deep learning model can support high-quality downstream data analysis and visualization, provide point-wise uncertainty quantification, and guarantee the distribution of the reconstructed data block follows the block's distribution representation.

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.

Subgroup studies can help identify risk factors that influence a detailed subpopulation of people and help provide insights into precision medicine. However, the complexity between patients of a specific disease and potential risk factors makes it hard for epidemiologists to find meaningful patterns. It is challenging for epidemiologists to go through all the possible different risk factors and analyze the relationships between risk factors. Therefore we developed an interactive visualization system called SG-RAD (SubGroup Risk factors Analysis and Discovery) to assist users in identifying subgroups, exploring said subgroup’s risk factors, and further investigating the relationship between them. Specifically, the system allows users to define the contrasting subgroup as well as the targeted variables, find notable patterns of risk factors and further investigate the risk factors’ influence within and outside the subgroup. We conduct a case study, to identify lifestyle habits that are considered as risk factors of gout in a subgroup within the same high-risk genetic group for gout in Taiwan Biobank’s population. Our system, SG-RAD, can assist our domain experts in finding patterns in a detailed group of people that can be considered as risk factors for disease and further investigate the risk factors influence within the subgroup.

Cosmological N-body simulation is an important tool for scientists to study the evolution of the universe. With the increase of computing power, billions of particles of high space-time fidelity can be simulated by supercomputers. However, limited computer storage can only hold a small subset of the simulation output for analysis, which makes the understanding of the underlying cosmological phenomena difficult. To alleviate the problem, we design an in-situ data reduction method for large-scale unstructured particle data. During the data generation phase, we use a combined k-dimensional partitioning and Gaussian mixture model approach to reduce the data by utilizing probability distributions. We offer a model evaluation criterion to examine the quality of the probabilistic distribution models, which allows us to identify and improve low-quality models. After the in-situ processing, the particle data size is greatly reduced, which satisfies the requirements from the domain experts. By comparing the astronomical attributes and visualizations of the reconstructed data with the raw data, we demonstrate the effectiveness of our in-situ particle data reduction technique.

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters, which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process. We performed two case studies, and discovered multiple new parameter configurations, which can trigger high cell polarization results in the original simulation model. We evaluated our results by comparing with the original simulation model outcomes as well as the findings from previous parameter analysis performed by our experts.

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

The analysis and visualization of data created from simulations on modern supercomputers is a daunting challenge because the incredible compute power of modern supercomputers allow scientists to generate datasets with very high spatial and temporal resolutions. The limited bandwidth and capacity of networking and storage devices connecting supercomputers to analysis machines become the major bottleneck for data analysis such that simply moving the whole dataset from the supercomputer to a data analysis machine is infeasible. A common approach to visualize high temporal resolution simulation datasets under constrained I/O is to reduce the sampling rate in the temporal domain while preserving the original spatial resolution at the time steps. Data interpolation between the sampled time steps alone may not be a viable option since it may suffer from large errors, especially when using a lower sampling rate. We present a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps. Our view-dependent proxy allows for a good trade off between compactly representing the time-varying data and leveraging temporal coherence within the data by utilizing interpolation between time steps, ray histograms, depth information, and codebooks. Our approach is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.

Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.

Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.

The size of large-scale scientific datasets created from simulations and computed on modern supercomputers continues to grow at a fast pace. A daunting challenge is to analyze and visualize these intractable datasets on commodity hardware. A recent and promising area of research is to replace the dataset with a distribution based proxy representation that summarizes scalar information into a much reduced memory footprint. Proposed representations subdivide the dataset into local blocks, where each block holds important statistical information, such as a histogram. A key drawback is that a distribution representing the scalar values in a block lacks spatial information. This manifests itself as large errors in visualization algorithms. We present a novel statistically-based representation by augmenting the block-wise distribution based representation with location information, called a value-based spatial distribution. Information from both spatial and scalar spaces are combined using Bayes' rule to accurately estimate the data value at a given spatial location. The representation is compact using the Gaussian Mixture Model. We show that our approach is able to preserve important features in the data and alleviate uncertainty.