Topic: Computational Science

Part of an AI framework called the Multi-Agent Design Assistant (MADA), LLNL scientists and collaborators are merging LLMs with simulation tools to interpret natural language prompts and using the platform to generate full physics simulation decks for LLNL’s MARBL multiphysics code.

As Computing’s ninth Fernbach Fellow, postdoctoral researcher Daniel Nichols will explore how AI can accelerate HPC and computational science under the mentorship of Harshitha Menon.

A new CASC paper proposes unity and clarity around foundation models in computational science, offering an implementation framework inspired by finite element methods.

Researchers at Brown University, LLNL, and Simula Research Laboratory have developed a new algorithm to help optimizers arrive at solutions in fewer iterations, saving valuable computing time.

A new cancer drug candidate developed by LLNL, BridgeBio Oncology Therapeutics, and the Frederick National Laboratory for Cancer Research has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

LLNL and Amazon Web Services are partnering to leverage the power of AI to enhance operations at the National Ignition Facility.

Using the Sierra supercomputer, an LLNL team has made significant progress in understanding how microscopic hot spots form in insensitive high explosives based on TATB.

Researchers from LLNL, in collaboration with other leading institutions, have successfully used an AI-driven platform to preemptively optimize an antibody to neutralize a broad diversity of SARS-CoV-2 variants.

A study led by LLNL scientists is providing new insights into the complex interactions between proteins and cell membranes, combining detailed molecular simulations and large-scale models.

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 latest issue of LLNL's magazine explains how the world’s most powerful supercomputer helps scientists safeguard the U.S. nuclear stockpile.

LLNL, Arizona State University and Michigan State University will dive deep into uncovering the compositions of 70 exoplanets through the Computing Grand Challenge Program, which allocates significant quantities of institutional computational resources to scientists to perform cutting-edge research.

LLNL's Bruce Hendrickson joins other HPC luminaries in this op-ed about the future of the field.

The latest episode of the Big Ideas Lab podcast investigates the use of artificial intelligence for drug discovery and other uses.

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.

The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.

The Generative Unconstrained Intelligent Drug Engineering (GUIDE) program accelerates development of medical countermeasure candidates to redefine biological defense.

Held for the first time in a hybrid format, the multi-day MFEM workshop drew participants from around the globe.

In a groundbreaking development for computational science, a team of Tri-Lab researchers has unveiled a revolutionary approach to molecular dynamics simulations using the Cerebras Wafer-Scale Engine, the world’s largest computer chip.

An iconic LLNL computer code that has saved the automobile industry billions of dollars is the focus for the newest episode of the Big Ideas Lab Podcast.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

A virtual reality training platform allows liver surgeons to prepare and train outside of the operating room.

Learn about the game-changing potential of El Capitan and discover how it will not only transform HPC and AI but also revolutionize scientific research across multiple domains.

A groundbreaking multidisciplinary team is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design.