arXiv subjects

Weidong Wen

Publications and source records attributed to Weidong Wen.

3 recordsLinked to original sources

arXiv2026

Hessian-Enhanced Alternating Frequency/Time method for Computing Resonance Backbone Curves

Computing resonance and anti-resonance backbone curves in complex nonlinear mechanical systems is of high engineering relevance but remains computationally challenging, especially for large finite-element (FE) models. Existing manifold-based approaches often rely on polynomial parameterizations, limiting their effectiveness for general smooth, non-polynomial nonlinearities. To overcome these limitations, we develop a direct optimization framework that employs a Lagrange multiplier formulation to determine the resonance backbone curve on the response surface constrained by the harmonic balance governing equations. Crucially, solving this formulation efficiently requires second-order sensitivity information. Therefore, the primary innovation of this work is the derivation of a analytical Hessian Tensor for generic $C^2$-continuous nonlinear elements. This is achieved by combining an extended Alternating Frequency/Time (AFT) method for computing second-order derivatives with local-coordinate tensor transformations. By integrating this analytical Hessian into the solver, the proposed framework ensures robust convergence and significantly reduces runtime, making it practical for large-scale models where numerical differentiation is computationally prohibitive. The method is validated on three benchmarks of increasing complexity: a two-degree-of-freedom (2-DOF) system with cubic nonlinearity, a beam with cubic stiffness or hyperbolic tangent (tanh) friction nonlinearities, and an industrial-scale finite element model of a compressor bladed disk (blisk) with a friction ring damper. Results demonstrate that the proposed framework accurately and efficiently computes both resonance and anti-resonance backbone curves, providing a robust frequency-domain tool for structures with non-polynomial nonlinearities.

physics.class-ph↗

arXiv2026

Precise Computation of Forced Response Backbone Curves of Frictional Structures Using Analytical Hessian Tensor of Contact Elements

Predicting the forced vibration response of nonlinear mechanical systems with friction is critical for engineering applications. Accurately determining the backbone curve of resonance peaks is pivotal for the design of friction devices. However, the prediction of these curves is computationally challenging owing to the nonconservative and nonsmooth nature of friction nonlinearity. Although techniques such as damped nonlinear normal modes (dNNMs) and phase resonance methods have been applied, they often suffer from convergence issues, and their computational accuracy is compromised under certain conditions. This study proposes a novel method for computing the forced response backbone curves of structures with frictional contact interfaces. The method accurately tracks the backbone curve through a parameter continuation scheme, formulated via Lagrange multipliers and accelerated by incorporating a derived analytical Hessian Tensor of contact elements. This approach yields highly accurate numerical results and enables numerical singularities on the curve to be identified and robustly traversed. The proposed method is validated using an Euler-Bernoulli beam finite-element model and a lumped-parameter blade-damper-blade model. The results demonstrate superior accuracy compared to conventional dNNMs and phase resonance methods, particularly in cases involving either high structural damping or strong frictional damping. This work provides a robust computational tool and presents a detailed comparative analysis that clarifies the applicability and limitations of the proposed and conventional methods.

physics.class-ph↗

arXiv2024

CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification

Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements. Activation sparsification can mitigate these resource challenges by reducing the number of activated neurons during inference. Existing methods typically employ thresholding-based sparsification based on the statistics of activation tensors. However, they do not model the impact of activation sparsification on performance, resulting in suboptimal performance degradation. To address the limitations, this paper reformulates the activation sparsification problem to explicitly capture the relationship between activation sparsity and model performance. Then, this paper proposes CHESS, a general activation sparsification approach via CHannel-wise thrEsholding and Selective Sparsification. First, channel-wise thresholding assigns a unique threshold to each activation channel in the feed-forward network (FFN) layers. Then, selective sparsification involves applying thresholding-based activation sparsification to specific layers within the attention modules. Finally, we detail the implementation of sparse kernels to accelerate LLM inference. Experimental results demonstrate that the proposed CHESS achieves lower performance degradation over eight downstream tasks while activating fewer parameters than existing methods, thus speeding up the LLM inference by up to 1.27x.

cs.CL↗