Topic: Scientific ML

SC24, held recently in Atlanta, was a landmark event, setting new records and demonstrating LLNL's unparalleled contributions to HPC innovation and impact.

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

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

AMS is a machine learning solution embedded into scientific applications to automatically replace fine-scale simulations with ancillary models.

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.

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.

The proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative will advance national security; attract and build a talented workforce; harness AI for scientific discovery; address energy challenges; develop technical expertise necessary for AI governance.

This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.

LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, HPC, and AI techniques.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art ML–based reconstruction tool for when high-quality computed tomography data is in low supply.

Merlin is an open-source workflow orchestration and coordination tool that makes it easy to build, run, and process large-scale workflows.

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.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.

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.

The addition of the spatial data flow accelerator into LLNL’s Livermore Computing Center is part of an effort to upgrade the Lab’s cognitive simulation (CogSim) program.

Open-source software has played a key role in paving the way for LLNL's ignition breakthrough, and will continue to help push the field forward.

A principal investigator at LLNL shares how machine learning on the world’s fastest systems catalyzed the lab’s breakthrough.

Adding machine learning and other artificial intelligence methods to the feedback cycle of experimentation and computer modeling can accelerate scientific discovery.

High performance computing was key to the December 5 breakthrough at the National Ignition Facility.