Automatic Parameter Sensitivities in Serac for Engineering Applications
Inside Livermore Lab via YouTube
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
Start speaking a new language. It’s just 3 weeks away.
Overview
AI, Data Science & Cloud Certificates from Google, IBM & Meta — 40% Off
One plan covers every Professional Certificate on Coursera. 40% off Coursera Plus Annual.
Unlock All Certificates
Learn about automatic parameter sensitivity calculations for topology and shape design optimization workflows in this technical presentation from the 2024 MFEM Workshop. Explore a framework built on MFEM infrastructure that provides abstractions for specifying, solving, coupling, and differentiating new PDEs for engineering applications. Discover recent developments in Serac including robust nonlinear solvers, Tribol library integration for contact enforcement, coupled thermal-mechanics, differentiable material model library, and checkpointing capabilities for transient adjoint calculations. Gain insights from LLNL researcher Michael Tupek on how these tools enable more efficient large-scale scientific simulations through high-order mathematical calculations and discretization algorithms.
Syllabus
MFEM Workshop 2024 | Automatic Parameter Sensitivities in Serac for Engineering Applications
Taught by
Inside Livermore Lab