7 papers
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…
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
Mahindra Rautela, Alexander Scheinker, Bradley Love +4
PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstrea…
A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling
Michael Grosskopf, Kellin Rumsey, Ayan Biswas +1
The next generation of Department of Energy supercomputers will be capable of exascale computation. For these machines, far more computation will be possible than that which can be…