12 papers
Accountability Asymmetry and Structural Trust in Autonomous AI Systems
Nathan DeBardeleben
Autonomous AI systems (such as AI agents) are increasingly being delegated operational work across scientific-computing infrastructure. Their assignments may begin with preparing a…
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben +4
Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying…
An Agentic Orchestration of Atomistic Simulations
Rahul Somasundaram, Adela Habib, Khanh Dang +12
Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agen…
DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
Mengdi Chu, Jiaxin Yang, Angus G. Forbes +4
Modeling temporal evolution is important to analyzing and reasoning about scientific phenomena, yet most machine learning methods provide deterministic forward predictions that ove…
In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
Daniel O'Malley, Christopher W. Johnson, Javier E. Santos +10
Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential for geothermal resource assess…
URSA: The Universal Research and Scientific Agent
Michael Grosskopf, Nathan Debardeleben, Russell Bent +10
Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These s…