3 papers
cs.LG2025
Nonlinear discretizations and Newton's method: characterizing stationary points of regression objectives
Conor Rowan
Second-order methods are emerging as promising alternatives to standard first-order optimizers such as gradient descent and ADAM for training neural networks. Though the advantages…
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…
cs.CE2025
Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method
Conor Rowan, Kurt Maute, Alireza Doostan
One use case of ``physics-informed neural networks'' (PINNs) is solution reconstruction, which aims to estimate the full-field state of a physical system from sparse measurements.…