Foundation Models in Computational Science

Computational science is at the forefront of modern technology, enabling groundbreaking simulations—from quantum molecular dynamics and magnetic fusion to complex wave phenomena. These advances have ushered in the era of digital twins, accelerating the design, build, test, and learn (DBTL) process across diverse applications. Yet even the most advanced simulations remain computationally expensive, often pushing high-performance computing systems to their limits. Fortunately, machine learning (ML) and artificial intelligence (AI) offer promising strategies to enhance simulation speed without sacrificing accuracy. In this talk, I will introduce several foundation models designed to accelerate computational simulations. I will critically evaluate these models, distinguishing between those that offer nice, yet incomplete gains and those that deliver genuine, comprehensive performance improvements. A highlight of the discussion will be the data-driven finite element method (DD-FEM), a robust foundation model whose effectiveness I will demonstrate across three applications: lattice-type structure design, steady Navier–Stokes porous media flow, and time-dependent 2D Burgers advective flow with multiple disturbances, achieving roughly 1000x speed-up and 100x scale-up with a relative error of O(1%).

Bio
Youngsoo Choi is a staff scientist at LLNL’s CASC group, where he develops efficient foundation models for computational science. His research focuses on creating surrogates and reduced-order models to accelerate time-critical simulations in areas such as inverse problems, design optimization, and uncertainty quantification. He has pioneered advanced ROM techniques—including machine learning-based nonlinear manifolds, space-time ROMs, component-wise ROM optimization, and latent space dynamics identification—and currently leads the libROM team in data-driven surrogate modeling. His contributions extend to open source projects such as libROM, pylibROM, LaghosROM, ScaleupROM, LaSDI, WLaSDI, tLaSDI, gLaSDI, NM-ROM, DD-NM-ROM, and GappyAE. Youngsoo earned his BS from Cornell and his PhD from Stanford, and he was a postdoc at Sandia National Laboratories and Stanford University before joining LLNL in 2017.
https://people.llnl.gov/choi15