ICECap looks to use exascale fusion simulations to pioneer digital design

A groundbreaking multidisciplinary team of LLNL researchers is combining the power of exascale computing with AI, advanced workflows and graphics processor (GPU)-acceleration to advance scientific innovation and revolutionize digital design. The project, called ICECap (Inertial Confinement on El Capitan), is a transformative approach to inertial confinement fusion (ICF) design optimization...

International workshop focuses on AI for critical infrastructure

On August 4, LLNL researchers Felipe Leno da Silva and Ruben Glatt hosted the AI for Critical Infrastructure workshop at the 33rd International Joint Conference on Artificial Intelligence (IJCAI) in Jeju, South Korea. Professors Wencong Su (University of Michigan – Dearborn) and Yi Wang (University of Hong Kong) joined them in organizing the workshop focused on exploring AI opportunities and...

Probing carbon capture, atom-by-atom

A team of scientists at LLNL has developed a machine-learning model to gain an atomic-level understanding of CO2 capture in amine-based sorbents. This innovative approach promises to enhance the efficiency of direct air capture (DAC) technologies, which are crucial for reducing the excessive amounts of CO2 already present in the atmosphere. The low cost of these sorbents has enabled several...

Department of Energy announces FASST initiative

On July 16, the Department of Energy formally announced the proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative via the web page www.energy.gov/fasst (with accompanying video and fact sheet). As stated on the web page, the speed and scale of the AI landscape are significant motivators for investing in strategic AI capabilities: “Without FASST...

AI, fusion, and national security with Brian Spears (VIDEO)

This episode of the Eye on AI podcast delves into the cutting-edge world of AI and high-performance computing with Brian Spears, director of LLNL's AI Innovation Incubator. The episode is presented here as a video with the following description: "Brian shares his experience in driving AI into national security science and managing the nation’s nuclear stockpile. With a PhD in mechanical...

The surprising places you’ll find machine learning (VIDEO)

LLNL data scientists are applying ML to real-world applications on multiple scales. A new DSI-funded video highlights research at the nanoscale (developing better water treatment methods by predicting the behavior of water molecules under the extremely confined conditions of nanotubes); mesoscale (determining the likelihood and location of a dangerous wildfire-causing phenomenon called arcing...

Machine learning optimizes high-power laser experiments

Commercial fusion energy plants and advanced compact radiation sources may rely on high-intensity, high-repetition rate lasers, capable of firing multiple times per second, but humans could be a limiting factor in reacting to changes at these shot rates. Applying advanced computing to this problem, a team of international scientists from LLNL, Fraunhofer Institute for Laser Technology (ILT)...

Igniting scientific discovery with AI and supercomputing (VIDEO)

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, high-performance computing (HPC), and AI techniques. The success of ignition marks a significant milestone in fusion energy research, and was facilitated in part by the precision simulations and rapid experimental data analysis only possible through...

Predicting climate change impacts on infrastructure (VIDEO)

At LLNL, electrical grid experts and climate scientists work together to bridge the gap between infrastructure and climate modeling. By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future. In a new video, LLNL computer scientist...

High-performance computing, AI and cognitive simulation helped LLNL conquer fusion ignition

For hundreds of LLNL scientists on the design, experimental, and modeling and simulation teams behind inertial confinement fusion (ICF) experiments at the National Ignition Facility, the results of the now-famous Dec. 5, 2022, ignition shot didn’t come as a complete surprise. The “crystal ball” that gave them increased pre-shot confidence in a breakthrough involved a combination of detailed...

Data science meets fusion (VIDEO)

LLNL’s historic fusion ignition achievement on December 5, 2022, was the first experiment to ever achieve net energy gain from nuclear fusion. However, the experiment’s result was not actually that surprising. A team leveraging data science techniques developed and used a landmark system for teaching artificial intelligence (AI) to incorporate and better account for different variables and...

Patent applies machine learning to industrial control systems

An industrial control system (ICS) is an automated network of devices that make up a complex industrial process. For example, a large-scale electrical grid may contain thousands of instruments, sensors, and controls that transfer and distribute power, along with computing systems that capture data transmitted across these devices. Monitoring the ICS network for new device connections, device...

Skywing: Open-source software aids collaborative autonomy applications

A new software developed at LLNL, and known as Skywing, provides domain scientists working to protect the nation’s critical infrastructure with a high-reliability, real-time software platform for collaborative autonomy applications. The U.S. modern critical infrastructure—from the electrical grid that sends power to homes to the pipelines that deliver water and natural gas and the railways...

New HPC4EI project to create 'digital twin' models for aerospace manufacturing

A partnership involving LLNL aimed at developing “digital twins” for producing aerospace components is one of six new projects funded under the HPC for Energy Innovation (HPC4EI) initiative, the Department of Energy’s Office of Energy Efficiency and Renewable Energy announced. Sponsored by the HPC4Manufacturing (HPC4Mfg) Program, one of the pillars of HPC4EI, the collaboration between LLNL...

An open-source, data-science toolkit for energy: GridDS

As the number of smart meters and the demand for energy is expected to increase by 50% by 2050, so will the amount of data those smart meters produce. While energy standards have enabled large-scale data collection and storage, maximizing this data to mitigate costs and consumer demand has been an ongoing focus of energy research. An LLNL team has developed GridDS—an open-source, data-science...

Defending U.S. critical infrastructure from nation-state cyberattacks

For many years, LLNL has been conducting research on cybersecurity, as well as defending its systems and networks from cyberattacks. The Lab has developed an array of capabilities to detect and defend against cyberintruders targeting IT networks and worked with government agencies and private-sector partners to share its cybersecurity knowledge to the wider cyberdefense community. LLNL has...

Laser-driven ion acceleration with deep learning

While advances in machine learning over the past decade have made significant impacts in applications such as image classification, natural language processing and pattern recognition, scientific endeavors have only just begun to leverage this technology. This is most notable in processing large quantities of data from experiments. Research conducted at LLNL is the first to apply neural...

The data-driven future of extreme physics

By applying modern machine learning and data science methods to “extreme” plasma physics, researchers can gain insight into our universe and find clues about creating a limitless amount of energy. In a recent perspective published in Nature, LLNL scientists and international collaborators outline key challenges and future directions in using machine learning and other data-driven techniques...

DOE announces five new energy projects at LLNL

The DOE today announced two rounds of awards for the High Performance Computing for Energy Innovation Program HPC4EI), including five projects at LLNL. HPC4EI connects industry with the computational resources and expertise of the DOE national laboratories to solve challenges in manufacturing, accelerate discovery and adoption of new materials and improve energy efficiency. The awards were...

Deep learning may provide solution for efficient charging, driving of autonomous electric vehicles

LLNL computer scientists and software engineers have developed a deep learning-based strategy to maximize electric vehicle (EV) ride-sharing services while reducing carbon emissions and the impact to the electrical grid, emphasizing autonomous EVs capable of offering 24-hour service. Read more at LLNL News.