LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art, machine learning ML–based reconstruction tool for when high-quality computed tomography data is in low supply.
Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.
The “crystal ball” that provided increased pre-shot confidence in LLNL's fusion ignition breakthrough involved a combination of detailed HPC design and a suite of methods combining physics-based simulation with machine learning—called cognitive simulation, or CogSim.
The report lays out a comprehensive vision for the DOE Office of Science and NNSA to expand their work in scientific use of AI by building on existing strengths in world-leading high performance computing systems and data infrastructure.
Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.