From the 1 of 12 linked papers with an AI index.
12 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…
Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty
Zhangyong Liang, Jie Hou, Huanhuan Gao +1
Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of admissible instances, while ea…
Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs
Zhangyong Liang, Huanhuan Gao
Enforcing prescribed global integral constraints in mesh-free neural PDE solvers is challenging in high-dimensional domains. Existing projection methods for spatial integrals are o…