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physics.comp-ph2026
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…
physics.comp-ph2026
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…
physics.flu-dyn2026
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…