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…
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural netw…
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…