Topic: Computational Science

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.

An LLNL mathematician and collaborators have developed a machine learning–based technique capable of automatically deriving a mathematical model for the motion of binary black holes from raw gravitational wave data.

In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms. The models track the steel from reheat-furnace dropout through the subsequent steps of rolling, cooling on the runout table, coiling and, finally, post-rolling cooling.

LLNL will lend its expertise in vaccine research—most recently from designing new antibodies and antiviral drugs for COVID-19—and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

A new version of the Energy Exascale Earth System Model (E3SM) is two times faster than its earlier version released in 2018. E3SM2 was released to the broader scientific community in September. The project is supported by the DOE's Office of Science in the Biological and Environmental Research Office.

The Center for Applied Scientific Computing and Data Science Institute welcomed a new academic partner to the 2021 Data Science Challenge program: the University of California Riverside campus. The intensive program has run for three years with UC Merced, and it tasks students with addressing a real-world scientific problem using data science techniques.

LLNL, in partnership with Los Alamos National Laboratory and Sandia National Laboratories, has awarded a subcontract to Dell Technologies for additional supercomputing systems to support the NNSA's nuclear deterrent mission. The contract will provide at least $40 million for more than 40 petaflops of expanded computing capacity for the NNSA Tri-Labs .

Computational biology is using HPC to rapidly design and develop ways to treat cancer and COVID. LLNL researcher Felice Lightstone discusses ATOM (Accelerated Therapeutic Opportunities in Medicine) in this edition of SC21 TV.

LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute, the virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.

From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.

LLNL and Purdue are partnering to speed up drug design using computational tools under the Accelerating Therapeutic Opportunities in Medicine project. LLNL researcher Jonathan Allen mentored students and two teaching assistants, introducing them to computationally driven drug discovery and designing predictive models for drug candidates.

The Center for Non-Perturbative Studies of Functional Materials under Non-Equilibrium Conditions advances high performance computing software to support novel materials discovery.

The Department of Energy announced awards of $3.7 million for 13 new High Performance Computing for Energy Innovation (HPC4EI) projects, including a collaboration involving LLNL targeted at improving CO₂ conversion.

LLNL engineers have demonstrated that aerodynamically integrated vehicle shapes decrease body-axis drag in a crosswind, creating large negative front pressures that effectively “pull” the vehicle forward against the wind, much like a sailboat.

Meeting virtually three times per week, 22 UC Merced students engaged with LLNL mentors and peers to address a real-world challenge problem, using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity.

Supported by the Advanced Simulation and Computing program, the open-source Axom project focuses on developing software infrastructure components that can be shared by HPC applications running on diverse computing platforms.

Our use of supercomputers is enabled by the codes developed to model and simulate complex physical phenomena on massively parallel architectures.

Using the Miranda code and the Ruby supercomputer, an LLNL team has taken a closer look at how nuclear weapon blasts close to the Earth’s surface create complications in their effects and apparent yields.

The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.

LLNL has turned to AMD and Penguin Computing to upgrade a supercomputer to help in the fight against the novel coronavirus. The computer's name is... Corona. The 2018 system, named for the total solar eclipse of 2017, will nearly double in peak performance to 4.5 peak petaflops.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.

COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.

In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.