85 citations · 202 across the 44 of their papers we have counts for
46 papers
A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
Alper Sahistan, Haichao Miao, Zhimin Li +3
Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are o…
Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
Haichao Miao, Zhimin Li, Kuangshi Ai +4
The ability to inspect, interpret, and communicate complex data is crucial for virtually any scientific endeavor, but often requires significant expertise outside the core domain r…
Exploring Bichronous Collaboration in Virtual Environments
Alexander Giovannelli, Shakiba Davari, Cherelle Connor +7
Virtual environments (VEs) empower geographically distributed teams to collaborate on a shared project regardless of time. Existing research has separately investigated collaborati…
Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Loïc Pottier, Konstantia Georgouli, Timothy S. Carpenter +8
Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scie…
ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use
Shusen Liu, Haichao Miao, Peer-Timo Bremer
While powerful and well-established, tools like ParaView present a steep learning curve that discourages many potential users. This work introduces ParaView-MCP, an autonomous agen…
Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality
Leonardo Pavanatto, Alexander Giovannelli, Brian Giera +3
Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between…
Investigating the Influence of Playback Interactivity during Guided Tours for Asynchronous Collaboration in Virtual Reality
Alexander Giovannelli, Leonardo Pavanatto, Shakiba Davari +5
Collaborative virtual environments allow workers to contribute to team projects across space and time. While much research has closely examined the problem of working in different…
Physics-Informed Transformation Toward Improving the Machine-Learned NLTE Models of ICF Simulations
Min Sang Cho, Paul E. Grabowski, Kowshik Thopalli +11
The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By repl…
HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
Zane Fink, Konstantinos Parasyris, Praneet Rathi +3
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scie…
Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data
Xuan Huang, Haichao Miao, Hyojin Kim +5
Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scien…
Enabling Additive Manufacturing Part Inspection of Digital Twins via Collaborative Virtual Reality
Vuthea Chheang, Saurabh Narain, Garrett Hooten +6
Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM…
A Virtual Environment for Collaborative Inspection in Additive Manufacturing
Vuthea Chheang, Brian Thomas Weston, Robert William Cerda +4
Additive manufacturing (AM) techniques have been used to enhance the design and fabrication of complex components for various applications in the medical, aerospace, energy, and co…
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Shusen Liu, Haichao Miao, Zhimin Li +3
With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented oppor…
Accelerating Flow Simulations using Online Dynamic Mode Decomposition
Seung Won Suh, Seung Whan Chung, Peer-Timo Bremer +1
We develop an on-the-fly reduced-order model (ROM) integrated with a flow simulation, gradually replacing a corresponding full-order model (FOM) of a physics solver. Unlike offline…
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
Matthew L. Olson, Shusen Liu, Rushil Anirudh +3
Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit tr…
Workflows Community Summit 2022: A Roadmap Revolution
Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala +102
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex s…
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning
Fikret Aydin, Konstantia Georgouli, Gautham Dharuman +5
Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the m…
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
Konstantia Georgouli, Helgi I Ingólfsson, Fikret Aydin +4
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelit…
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer +2
Improving the accuracy of deep neural networks (DNNs) on out-of-distribution (OOD) data is critical to an acceptance of deep learning (DL) in real world applications. It has been o…
Single Model Uncertainty Estimation via Stochastic Data Centering
Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy +1
We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a str…
Data-driven model for divertor plasma detachment prediction
Ben Zhu, Menglong Zhao, Harsh Bhatia +5
We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our a…
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Zhimin Li, Shusen Liu, Xin Yu +5
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks have been…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Ankita Shukla, Rushil Anirudh, Eugene Kur +5
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hypers…