Topic: UQ and Statistics

In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

Highlights include ML techniques for computed tomography, a scalable Gaussian process framework, safe and trustworthy AI, and autonomous multiscale simulations.

The team used a Bayesian approach to quantify metal strength uncertainty with tantalum and two common explosive materials and integrated it into a coupled metal/high-explosive model.

A CASC researcher and collaborators study model failure and resilience in a paper accepted to the 2024 International Conference on Machine Learning.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

Highlights include complex simulation codes, uncertainty quantification, discrete event simulation, and the Unify file system.

The NSDE project is focused on research and development of nonlinear solvers and sensitivity analysis techniques for nonlinear, time-dependent, and steady-state partial differential equations.

The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.