From the 1 of 15 linked papers with an AI index.
15 papers
Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers
Zhangyong Liang, Huanhuan Gao
Energy natural gradient descent (ENGD) aligns parameter updates with the curvature of an underlying function-space energy, but existing formulations assume an unconstrained Euclide…
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
Zhangyong Liang, Huanhuan Gao
The paper proposes Disentangled Latent Dynamics Manifold Fusion (DLDMF), a physics‑informed neural framework that separates space, time, and PDE parameters, maps parameters to a co…
Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations
Zhangyong Liang, Huanhuan Gao
Cohesive Zone Models (CZMs) are widely used to simulate interface fracture, delamination, adhesive failure, and fiber--matrix debonding in aerospace composite structures. In implic…
Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories
Zhangyong Liang, Ji Zhang, Huanhuan Gao
Classical data valuation defines a data point's value through the finite marginal contribution , but estimating this quantity over coalitions requires repeated…
Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations
Zhangyong Liang, Huanhuan Gao
We propose HIN-LRI, a hybrid framework that augments a classical numerical solver with a neural operator trained to correct the solver's structured truncation error. A base low-reg…
Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains
Zhangyong Liang, Huanhuan Gao
This paper introduces the Stochastic-Dimension Frozen Sampled Neural Network (SD-FSNN), a novel computational framework for solving high-dimensional Gross-Pitaevskii equation (GPE)…