Topic: Computational Science

LLNL researchers and collaborators have combined machine learning, 3D printing, and HPC simulations to accurately model blood flow in the aorta.

This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.

In the HPC4EI project, LLNL and OxEon Energy will reduce the number of reactor tubes used to convert natural gas to liquid fuel, to lower cost and increase performance of synthetic fuel production.

Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

A team led by an LLNL computer scientist proposes a deep learning approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using artificial intelligence.

To help accelerate discovery of therapeutic antibodies or antiviral drugs for SARS-CoV-2, LLNL has launched a searchable data portal to share its COVID-19 research with scientists and the public.

LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50.

Combining computer simulations with ultra-high-speed X-ray imaging, LLNL researchers have discovered a way to reduce defects in parts built through a laser-based metal 3D-printing process.

In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory."

An LLNL team developed ML tools that extract and structure information from the text and figures of nanomaterials articles using NLP, image analysis, computer vision, and visualization techniques.

LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2.

Alyson Fox is a math geek. She has three degrees in the subject—including a Ph.D. in Applied Mathematics from the University of Colorado at Boulder—and her passion for solving complex challenges drives her work at LLNL’s Center for Applied Scientific Computing (CASC).

The early-March event was the third annual WiDS Livermore event, featuring speakers, a career panel, mentoring, and a livestream.

LLNL has infrastructure, unique research capabilities, and a dedicated team of scientists and engineers supporting the fight against COVID-19.

LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.

The White House announced the COVID-19 HPC Consortium to provide access to the world’s most powerful HPC resources that can advance the pace of scientific discovery in the fight to stop the virus.

LLNL bested more than two dozen teams to place first overall in Challenge 1 of the DOE Grid Optimization Competition, aimed at developing a more reliable, resilient, and secure U.S. electrical grid.

On January 31, 2020, the Sequoia supercomputer and its file system were decommissioned after nearly 8 years of remarkable service and achievements.

Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for ICF.

A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

Jorge Castro Morales likes having different responsibilities at work. He says, “I’m honored to be working with a diverse team of multidisciplinary experts to resolve very complex problems on a daily basis.”

The paper describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins and interactions with lipids.

Twelve projects are awarded funding for the High Performance Computing for Energy Innovation Program, which leverages DOE’s HPC facilities to improve energy efficiency and manufacturing processes.