Computational Engineering


Modeling for Prediction and Insight

Modeling, simulation, and data science are collaborative endeavors with participation across the Engineering divisions—in particular the Computational Engineering Division, the National Security Engineering Division, and the Defense Technologies Engineering Division. Collectively, our teams advance modeling and simulation, data-analytic tools, and entire fields of study, such as collaborative autonomy—all with the aid of LLNL’s massive computing facilities.

Our goal is to create and utilize computational models to provide insights into physical systems for the Laboratory and its sponsors. Given the breadth of R&D concerns at LLNL, Engineering relies upon a broad portfolio of software tools to generate and execute these models. A key set of capabilities for modeling hydro-structural response, fluid flow, and electromagnetics are internally developed to meet our specialized needs and provide rapid support to users. Other modeling needs are best met by using commercially available software, for which we also have expert users.

Other modeling needs are met through data science techniques. Vast data sets, whether gathered by physical observations or simulations, can now be computationally digested to generate models revealing essential relationships within the data. These models can also support prediction of scenarios not already observed. In the emerging field of collaborative autonomy, data-analytic techniques are used both to distill the best aggregate measurements from a collection of mobile sensor platforms and to collectively decide how to reposition themselves to achieve improved measurements.

Computational engineering at LLNL flourishes through the broad array of computing platforms available. LLNL’s High Performance Computing (HPC) facility houses several of the Top 500 most powerful computers worldwide, and in 2023 will include one of the world’s first exascale computing platforms. These platforms are supported by an entire ecosystem, including visualization platforms, storage arrays, and high-bandwidth networks. At other times, specialized hardware such as FPGAs (field programmable gate arrays) is utilized when best positioned to meet the performance needs of a particular application.

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Project Highlights

From computer simulations to compact algorithms, we harness LLNL’s high-performance computing capabilities to address issues of national security and to solve problems with global impact.

A graphic illustrating a new deep reinforcement learning framework for symbolic regression above headshots of the team that developed it.

Applying Deep Reinforcement Learning to Symbolic Regression Problems

In a paper from March 2021, LLNL computer scientists explained a new framework and an accompanying visualization tool that leverages deep reinforcement learning for symbolic regression problems. Read Full Article

A series of images showing the pressure and temperature fields that are generated when a shock wave collapses a single pore in beta-HMX crystal.

New Computer Simulations Reveal New Information about High Explosives

Engineers in the Computational Engineering Division unveiled computer simulations exploring the effects of shock waves on crystalline HMX in the May 2015 issue of the Journal of Applied Physics. Read Full Article

LLNL scientists explain how they used antibodies from the nearly 20-year-old SARS-1 outbreak to engineer antibodies for COVID-19.

LLNL GUIDE Rapid Response Platform Employed by DOD in Omicron Response

Between 2020 and 2022, engineers at LLNL used machine learning to create antibodies against Omicron, which has had immediate clinical interest and investment. .Read Full Article

Related Facilities and Centers 

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