2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
Lena Podina, Diba Darooneh, Joshveer Grewal +1
In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importance. Symbolic regression is a use…
Conformalized Physics-Informed Neural Networks
Lena Podina, Mahdi Torabi Rad, Mohammad Kohandel
Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neu…
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
Amartya Mukherjee, Melissa M. Stadt, Lena Podina +2
Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to p…
A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data
Lena Podina, Brydon Eastman, Mohammad Kohandel
Given ample experimental data from a system governed by differential equations, it is possible to use deep learning techniques to construct the underlying differential operators. I…