3 papers
physics.flu-dyn2026
Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Samantha Friess, Aviral Prakash, John A. Evans
Neural networks offer highly expressive turbulence closures, yet their complexity obscures the physical mechanisms they aim to model, and their computational cost can limit their t…
eess.IV2025
Variational volume reconstruction with the Deep Ritz Method
Conor Rowan, Sumedh Soman, John A. Evans
We present a novel approach to variational volume reconstruction from sparse, noisy slice data using the Deep Ritz method. Motivated by biomedical imaging applications such as MRI-…
math.NA2025
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
Conor Rowan, John Evans, Kurt Maute +1
From characterizing the speed of a thermal system's response to computing natural modes of vibration, eigenvalue analysis is ubiquitous in engineering. In spite of this, eigenvalue…