DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
A data-driven model based on the GENERIC formalism learns the nonlinear thermodynamic master equation of an open quantum system, recovering Hamiltonian and coupling operators from Bloch-vector trajectories.
SINO learns PDE operators from limited data using spectral features from frequency indices, a Pi-block for nonlinearities, and a low-pass filter, achieving 1-2 orders of magnitude better accuracy than prior methods on 2D/3D benchmarks.
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
citing papers explorer
-
Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.
-
Learning thermodynamic master equations for open quantum systems
A data-driven model based on the GENERIC formalism learns the nonlinear thermodynamic master equation of an open quantum system, recovering Hamiltonian and coupling operators from Bloch-vector trajectories.
-
Spectral-inspired Operator Learning with Limited Data and Unknown Physics
SINO learns PDE operators from limited data using spectral features from frequency indices, a Pi-block for nonlinearities, and a low-pass filter, achieving 1-2 orders of magnitude better accuracy than prior methods on 2D/3D benchmarks.
-
Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
-
DustNET: enabling machine learning and AI models of dusty plasmas
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.