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Berkeley researchers have set a new benchmark in fluid flow modeling with their Chombo-Crunch software, capturing unprecedented detail of how fluids move deep underground—a breakthrough that could transform energy production, battery safety, and water use in manufacturing.
Berkeley Lab AMCR researchers have developed a machine learning framework that dramatically accelerates battery lifespan predictions—using far fewer experiments—by combining expert insights with advanced AI to target only the most informative tests.
A collaboration among Berkeley Lab researchers and others has led to the development of ML models that enable real-time predictions of particle heating in fusion plasmas, potentially transforming how researchers conduct physics experiments.
Berkeley researchers have set a new benchmark in fluid flow modeling with their Chombo-Crunch software, capturing unprecedented detail of how fluids move deep underground—a breakthrough that could transform energy production, battery safety, and water use in manufacturing.
Berkeley Lab AMCR researchers have developed a machine learning framework that dramatically accelerates battery lifespan predictions—using far fewer experiments—by combining expert insights with advanced AI to target only the most informative tests.
Berkeley Lab researchers have developed a GPU-accelerated method for building large H2 matrices, enabling much faster and more memory-efficient scientific computations—a breakthrough recognized with the Best Paper award at the 26th IEEE IPDSEC 2025.
Berkeley Lab scientists have developed a faster method for topological data analysis, improving efficiency in uncovering hidden structures in complex datasets. This breakthrough could transform data analysis in fields like machine learning and biology.
A collaboration among Berkeley Lab researchers and others has led to the development of ML models that enable real-time predictions of particle heating in fusion plasmas, potentially transforming how researchers conduct physics experiments.
Stefan Wild has been named a 2025 SIAM Fellow. This recognition celebrates his groundbreaking work in derivative-free optimization, which has made significant impacts across multiple scientific and engineering disciplines.
SciData transforms data-driven discovery and understanding through the development and application of novel data science methods, technologies, and infrastructures with scientific partners.
The Applied Mathematics & Computational Research Division conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture, and high-performance software implementation.
The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is an integrated, cross-disciplinary center aimed at inventing, developing, and delivering the fundamental new mathematics required to capitalize on experimental investigations at scientific facilities.
The Quantum Systems Accelerator is catalyzing national leadership in quantum information science to co-design the algorithms, quantum devices, and engineering solutions needed to deliver certified quantum advantage in Department of Energy scientific applications. Berkeley Lab leads the center. Sandia National Laboratories is the lead partner.
