HYCO is a hybrid modeling framework that co-trains physics-based and data-driven PDE models through mutual regularization, interpreted as a Nash equilibrium problem.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.
citing papers explorer
-
HYCO: A Formalism for Hybrid-Cooperative PDE Modelling
HYCO is a hybrid modeling framework that co-trains physics-based and data-driven PDE models through mutual regularization, interpreted as a Nash equilibrium problem.
-
Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
-
Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects
A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.