2 papers
physics.comp-ph2026
A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin Park, Grant Bruer, Huseyin Tuna Erdinc +2
Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from…
cs.LG2025
When are dynamical systems learned from time series data statistically accurate?
Jeongjin Park, Nicole Yang, Nisha Chandramoorthy
Conventional notions of generalization often fail to describe the ability of learned models to capture meaningful information from dynamical data. A neural network that learns comp…