2 citations · 5 across the 12 of their papers we have counts for
4 papers · 1 filter
Forward Gradients for Data-Driven CFD Wall Modeling
Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan +1
Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications. However, the practical use is often li…
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +5
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurrica…
Understanding Automatic Differentiation Pitfalls
Jan Hückelheim, Harshitha Menon, William Moses +3
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accura…
Model Checking Race-freedom When "Sequential Consistency for Data-race-free Programs" is Guaranteed
Wenhao Wu, Jan Hückelheim, Paul D. Hovland +2
Many parallel programming models guarantee that if all sequentially consistent (SC) executions of a program are free of data races, then all executions of the program will appear t…