5 papers
Diff-NekRS: A Scalable Differentiable Framework for Multi-Timestep Solver-in-the-Loop Training
Junoh Jung, Riccardo Balin, Bethany Lusch +1
Hybrid physics-machine-learning solvers improve under-resolved simulations by embedding trainable corrections into the time integration. During autoregressive inference, repeated s…
A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data
Junoh Jung, David Lenz, Emil Constantinescu +1
Continuous approximations of flow data are useful for downstream analysis, differentiation, and visualization, but purely data-driven reconstructions do not, in general, preserve t…
Learning Differentiable Weak-Form Corrections to Accelerate Finite Element Simulations
Junoh Jung, Emil Constantinescu
We present a differentiable weak-form learning approach for accelerating finite element simulations. Rather than introducing black-box source terms in the strong form of the govern…
Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
Pi-Yueh Chuang, Ahmed Attia, Emil Constantinescu
This work introduces a mathematical framework for estimating the space-parameter sensitivity of random samples in arbitrary dimensions. Such sensitivity effectively acts as gradien…
Electron dynamics in extended systems within real-time time-dependent density functional theory
Alina Kononov, Cheng-Wei Lee, Tatiane Pereira dos Santos +10
Due to a beneficial balance of computational cost and accuracy, real-time time-dependent density functional theory has emerged as a promising first-principles framework to describe…