Topic: ODE Methods

A new method defines a formal specification for convergence, which can be used to derive a set of machine-checkable conditions to guarantee a convergent solution to a differential equation.

Can novel mathematical algorithms help scientific simulations leverage hardware designed for machine learning? A team from LLNL’s Center for Applied Scientific Computing aimed to find out.

An LLNL Distinguished Member of Technical Staff, Carol Woodward consults on a diverse array of projects at the Lab and beyond. “It’s nice because it means I can work at the same place and not just do one thing for a long time,” she says.

This project solves initial value problems for ODE systems, sensitivity analysis capabilities, additive Runge-Kutta methods, DAE systems, and nonlinear algebraic systems.

The prestigious award is handed out every two years and recognizes outstanding contributions to the development and use of mathematical and computational tools and methods for the solution of science and engineering problems.

As Computing’s fifth Fernbach Fellow, postdoctoral researcher Steven Roberts will develop, analyze, and implement new time integration methods.

Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

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

These Fortran solvers tackle the initial value problem for ODE systems. The collection includes solvers for systems given in both explicit and linearly implicit forms.