1 citations · 1 across the 10 of their papers we have counts for
12 papers
Geometry-aware LegONet for PDE Learning on Arbitrary Domains
Jiahao Zhang, Yueqi Wang, Guang Lin
Learned PDE solvers often entangle governing operators with the geometry, boundary conditions, and discretization used for training. This limits reuse when the same physics is pose…
A Hyperbolic Neural Closure for M1 Radiation Transfer
Bongseok Kim, Jiahao Zhang, Johannes Krotz +3
In radiation transfer simulations, an M1 method achieves substantial computational savings by replacing the full angular transport equation with a low-order moment system. Because…
LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning
Jiahao Zhang, Yueqi Wang, Guang Lin
Learned PDE solvers are often trained as monolithic surrogates for a specific equation, boundary condition and discretization. This makes them difficult to reuse when mechanisms ch…
TempoFit: Plug-and-Play Layer-Wise Temporal KV Memory for Long-Horizon Vision-Language-Action Manipulation
Jun Sun, Boyu Yang, Jiahao Zhang +7
Pretrained Vision-Language-Action (VLA) policies have achieved strong single-step manipulation, but their inference remains largely memoryless, which is brittle in non-Markovian lo…
Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations
Bongseok Kim, Jiahao Zhang, Guang Lin
Partial differential equations (PDEs) form a central component of scientific computing. Among recent advances in deep learning, evolutionary neural networks have been developed to…
Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
Binghang Lu, Jiahao Zhang, Guang Lin
Physics-informed neural networks and neural operators often suffer from severe optimization difficulties caused by ill-conditioned gradients, multi-scale spectral behavior, and sti…
BEKAN: Boundary condition-guaranteed evolutionary Kolmogorov-Arnold networks with radial basis functions for solving PDE problems
Bongseok Kim, Jiahao Zhang, Guang Lin
Deep learning has gained attention for solving PDEs, but the black-box nature of neural networks hinders precise enforcement of boundary conditions. To address this, we propose a b…
Low-Rank Evolutionary Deep Neural Networks via Adaptive Tangent-Space Reduction
Jiahao Zhang, Shiheng Zhang, Guang Lin
Evolutionary deep neural networks (EDNNs) solve time-dependent partial differential equations by evolving the neural-network parameters sequentially in time through a local least-s…
Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems
Guang Lin, Changhong Mou, Jiahao Zhang
We introduce evolutionary Kolmogorov-Arnold Networks (EvoKAN), a novel framework for solving complex partial differential equations (PDEs). EvoKAN builds on Kolmogorov-Arnold Netwo…
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
Jiahao Zhang, Christian Moya, Guang Lin
Optimizing the learning rate remains a critical challenge in machine learning, essential for achieving model stability and efficient convergence. The Vector Auxiliary Variable (VAV…
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
Shiheng Zhang, Jiahao Zhang, Jie Shen +1
We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved ali…
Direct computation of stresses in linear elasticity
Weifeng Qiu, Minglei Wang, Jiahao Zhang
We present a new finite element method based on the formulation introduced by Philippe G.~Ciarlet and Patrick Ciarlet, Jr. in [{\em Math. Models Methods Appl. Sci., 15 (2005), pp.…