Scott Klasky

The Data and AI Systems Section of the Computer Science and Mathematics Division at Oak Ridge National Laboratory conducts research on the data and artificial intelligence foundations of scientific discovery, shortening and automating the path from instrument and simulation to insight at the scale of leadership computing facilities. This report collects 27 project highlights from fiscal year 2026, organized by the section's five groups, namely Data Engineering, Learning Systems, Performance Engineering, Visualization, and Workflow Systems, each introduced by its group leader. Every highlight is written by the people who did the work and names someone who can be contacted about it directly. The highlights cover AI methods co-designed with the platforms that run them, from spiking and neuromorphic models to energy-efficient large language models. They also cover data reduction and campaign management that make scientific datasets AI-ready, uncertainty-aware and AI-assisted visualization at leadership scale, and workflows that couple experiment, simulation, and AI into closed loops needing little human intervention. Reported applications span fusion energy, materials science, electron microscopy, cosmology, plant phenotyping, health analytics, and national security. Much of our current agenda runs through the Department of Energy's Genesis Mission, where we contribute to the American Science Cloud and to the Transformational AI Models Consortium.

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Large-scale simulations of nonlinear partial differential equations (PDEs) that exhibit strongly transient behavior and pattern-forming dynamics produce enormous amount of data, which, even with modern storage system, cannot be stored for later curation. Current I/O strategies either write dense time series of snapshots, which is often prohibitive in I/O and storage, or store a few checkpoints that enable restart but incur expensive recomputation cost and provide no control on post-restart error growth, especially when lossy compression is used. Moreover, most if not all existing strategies take no account of the actual physical state of the system. Here, we present a simple physics-aware I/O framework in which a lowcost shadow system adaptively triggers lossy checkpoints when the shadow system deviates from the fine-scale simulation. The shadow system can be a coarsened replica of the fine-scale simulation that evolves concurrently. This means that checkpoints are taken based on the physical state of the system: fewer checkpoints are triggered when the system is quiescent while more are taken when the system undergoes a rapid change. This type of behavior is observed in many systems such as Brusselator and FitzHugh-Nagumo. We illustrate that our framework maintains stable restarts, keeps fine-scale restart errors bounded by shadow errors, and reconstructs the time history with significantly lower error and storage than interpolating fixed-interval snapshots, at only modest additional runtime overhead.