Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an *in situ* infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.

# Topic: *Computational Math*

Livermore’s *hypre *library of solvers makes larger, more detailed simulations possible by solving problems faster than ever before. It offers one of the most comprehensive suites of scalable parallel linear solvers available for large-scale scientific simulation.

As demonstrated by CASC computer scientists, LLNL's innovative data-driven machine learning techniques teach computers to solve real-world problems.

LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.

Profile of LLNL's Lori Diachin, who has over 25 years experience in applied mathematics research including mesh quality improvement, mesh component software, numerical methods, and parallel computing.

LLNL heads to the SIAM Conference on Computational Science and Engineering (CSE19) in Spokane, Washington, on February 25 to March 1, 2019.

Highlights include debris an shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.

Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

LLNL has named Will Pazner as Computation’s third Sidney Fernbach Postdoctoral Fellow in the Computing Sciences.

Highlights include the latest work with RAJA, the Exascale Computing Project, algebraic multigrid preconditioners, and OpenMP.

In response to a DOE grid optimization challenge, the LLNL-led gollnlp team is developing the mathematical, computational, and software components needed to solve problems of the real-world power grid.

Highlights include complex simulation codes, uncertainty quantification, discrete event simulation, and the Unify file system.

Highlights include recent LDRD projects, Livermore Tomography Tools, our work with the open-source software community, fault recovery, and CEED.

Highlights include Computation’s annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.

The code GEFIE-QUAD (gratings electric field integral equation on quadrilateral grids) is a first-principles simulation method to model the interaction of laser light with diffraction gratings, and to determine how grating imperfections can affect the performance of the compressor in a CPA laser system. GEFIE-QUAD gives scientists a powerful simulation tool to predict the performance of a realistic laser compressor.

Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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

The Extreme Resilient Discretization project (ExReDi) was established to address these challenges for algorithms common for fluid and plasma simulations.

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

Newly developed mathematical techniques reveal important tools for data mining analysis.

Livermore researchers have developed an algorithm for the numerical solution of a phase-field model of microstructure evolution in polycrystalline materials. The system of equations includes a local order parameter, a quaternion representation of local orientation, and species composition. The approach is based on a finite volume discretization and an implicit time-stepping algorithm. Recent developments have been focused on modeling solidification in binary alloys, coupled with CALPHAD methodology.

LLNL researchers are developing a truly scalable first-principles molecular dynamics algorithm with O(N) complexity and controllable accuracy, capable of simulating systems of sizes that were previously impossible with this degree of accuracy.

GLVis is a lightweight OpenGL-based tool for accurate and flexible finite element visualization. It is based on MFEM, a finite element library developed at LLNL. GLVis provides interactive visualizations of general finite element meshes and solutions, both in serial and in parallel. It encodes a large amount of parallel finite element domain-specific knowledge; e.g., it allows the user to view parallel meshes as one piece, but it also gives them the ability to isolate each component and observe it individually. It provides support for arbitrary high-order and NURBS meshes (NURBS allow more accurate geometric representation) and accepts multiple socket connections so that the user may have multiple fully-functional visualizations open at one time. GLVis can also run a batch sequence, or a series of commands, which gives the user precise control over visualizations and enables them to easily generate animations.

High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.

LLNL researchers are testing and enhancing a neutral particle transport code and the algorithm on which the code relies to ensure that they successfully scale to larger and more complex computing systems.