Under a recently funded project, researchers at LLNL and the Massachusetts Institute of Technology (MIT) will address the challenge of efficiently differentiating large-scale applications for the Department of Energy by building on advances in LLNL’s MFEM finite element library and MIT’s Enzyme AD tool. The team’s project will address the challenge of efficiently differentiating large-scale DOE applications—predicting how adjustments in design parameters will impact the output of a code. Knowledge of optimal outputs is increasingly needed for complex simulation codes to be used for design optimization, machine learning, uncertainty quantification and sensitivity analysis, among other applications. While automatic differentiation (AD) has made the differentiation process easier, traditional AD tools require significant changes that are not feasible for many existing large-scale DOE applications. Read more about the project at LLNL News.