82 citations · 231 across the 49 of their papers we have counts for
12 papers · 1 filter
Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling
Zaid Abulawi, Rui Hu, Prasanna Balaprakash +1
Accurate predictions and uncertainty quantification (UQ) are essential for decision-making in risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficie…
Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning
Julian Barra, Shayan Shahbazi, Anthony Birri +6
Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mi…
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Hongwei Jin, George Papadimitriou, Krishnan Raghavan +5
Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. T…
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutio…
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
Hongwei Jin, Prasanna Balaprakash, Allen Zou +5
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.…
Advancing Anomaly Detection in Computational Workflows with Active Learning
Krishnan Raghavan, George Papadimitriou, Hongwei Jin +4
A computational workflow, also known as workflow, consists of tasks that are executed in a certain order to attain a specific computational campaign. Computational workflows are co…
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier
Aristeidis Tsaris, Chengming Zhang, Xiao Wang +9
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence length…
Network architecture search of X-ray based scientific applications
Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer +2
X-ray and electron diffraction-based microscopy use bragg peak detection and ptychography to perform 3-D imaging at an atomic resolution. Typically, these techniques are implemente…
AI Competitions and Benchmarks: Dataset Development
Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn +7
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting…
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Yixuan Sun, Ololade Sowunmi, Romain Egele +3
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjectiv…
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
Romain Egele, Felix Mohr, Tom Viering +1
To reach high performance with deep learning, hyperparameter optimization (HPO) is essential. This process is usually time-consuming due to costly evaluations of neural networks. E…
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability
Xiao Wang, Siyan Liu, Aristeidis Tsaris +8
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by le…