arXiv subjects

Changyu Shen

Publications and source records attributed to Changyu Shen.

8 recordsLinked to original sources

arXiv2026

PIML-OFEM: A New Large-Scale Structural Analysis Method Based on Problem-Independent Machine Learning and Overlapping Finite Element Technique

High-resolution analysis and design of large-scale heterogeneous structures require accurate reduced-order models and efficient online computation. Existing multiscale methods must repeatedly construct local basis functions for different material distributions, whereas substructure-based problem-independent machine learning (PIML) methods can be limited by prescribed boundary displacement interpolation. We propose PIML-OFEM, an overlapping finite element method accelerated by problem-independent machine learning. Each substructure retains only its corner-node degrees of freedom. Oversampled numerical basis functions are constructed by solving local elasticity problems on extended domains and restricting the solutions to the target substructure, eliminating prescribed displacement interpolation on its boundary. Independently constructed local bases are blended through a partition-of-unity overlapping formulation to obtain a globally continuous displacement field. A U-Net learns the mapping from local Young's modulus distributions to numerical basis functions, replacing repeated online local solves and allowing the model to be reused across load cases and global boundary conditions. Numerical examples show close agreement with fine-scale finite element results in displacement and elemental strain energy. PIML-OFEM reduces online computational cost relative to direct finite element analysis and improves accuracy over PIML substructure models based on linear boundary interpolation. In topology optimization, the method supports stable high-resolution iterations with small filter radii and preserves fine-scale features, including local patterns resembling rank-2 microstructures. The framework provides an efficient physics-data approach for large-scale heterogeneous structural analysis and high-resolution topology optimization.

math.NA↗

arXiv2026

Laser-Scrawled Random Plasmonic Metasurface in Nanoseconds for Physical Unclonable Functions

Randomness in optical systems emerges as a powerful resource for generating complex, non-deterministic light-matter interactions. In particular, random plasmonic metasurfaces harness nanoscale disorder to produce unique and irreproducible optical responses, positioning them as an ideal platform for physical unclonable function in secure optical authentication. However, realizing such random metasurfaces in a rapid, scalable, and chemical-free manner for optical PUFs remains challenging. Here, we introduce a nanosecond pulsed laser scribing method for one-step fabrication of a robust random plasmonic metasurface physical unclonable function. By delivering spatially localized, ultrafast energy bursts, this technique harnesses naturally occurring instability to generate stochastic plasmonic nanostructures in nanoseconds. The unique plasmonic metasurfaces are effectively transformed into a macroscopic, non-replicable optical fingerprint via morphology-dependent resonance at the nanoscale, enabling low-cost and fast readout. Leveraging the wavelength-selective plasmonic response, we present a multidimensional multiplexing strategy that expands the challenge response pairs space and encoding capacity by 5-fold via topography and RGB multiplexing. The resulting plasmonic keys exhibit good bit uniformity (average: 0.500), high uniqueness (inter-Hamming distance: 0.499), and large capacity (~28000 bits per PUF), with strong environmental stability and resistance to reverse nanofabrication. This work demonstrates how fast laser induced stochasticity can be rationally harnessed and engineered for optical PUFs, opening pathways toward disorder-enabled photonic devices.

physics.optics↗

arXiv2024

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows

Physics-Informed Neural Networks (PINN) has evolved into a powerful tool for solving partial differential equations, which has been applied to various fields such as energy, environment, en-gineering, etc. When utilizing PINN to solve partial differential equations, it is common to rely on Automatic Differentiation (AD) to compute the residuals of the governing equations. This can lead to certain precision losses, thus affecting the accuracy of the network prediction. This paper pro-poses a Finite Volume Physics-Informed Neural Network (FV-PINN), designed to address steady-state problems of incompressible flow. This method divides the solution domain into mul-tiple grids. Instead of calculating the residuals of the Navier-Stokes equations at collocation points within the grid, as is common in traditional PINNs, this approach evaluates them at Gaussian in-tegral points on the grid boundaries using Gauss's theorem. The loss function is constructed using the Gaussian integral method, and the differentiation order for velocity is reduced. To validate the effectiveness of this approach, we predict the velocity and pressure fields for two typical examples in fluid topology optimization. The results are compared with commercial software COMSOL, which indicates that FVI-PINN significantly improves the prediction accuracy of both the velocity and pressure fields while accelerating the training speed of the network.

physics.flu-dyn↗

arXiv2024

Using Principal Progression Rate to Quantify and Compare Disease Progression in Comparative Studies

In comparative studies of progressive diseases, such as randomized controlled trials (RCTs), the mean Change From Baseline (CFB) of a continuous outcome at a pre-specified follow-up time across subjects in the target population is a standard estimand used to summarize the overall disease progression. Despite its simplicity in interpretation, the mean CFB may not efficiently capture important features of the trajectory of the mean outcome relevant to the evaluation of the treatment effect of an intervention. Additionally, the estimation of the mean CFB does not use all longitudinal data points. To address these limitations, we propose a class of estimands called Principal Progression Rate (PPR). The PPR is a weighted average of local or instantaneous slope of the trajectory of the population mean during the follow-up. The flexibility of the weight function allows the PPR to cover a broad class of intuitive estimands, including the mean CFB, the slope of ordinary least-square fit to the trajectory, and the area under the curve. We showed that properly chosen PPRs can enhance statistical power over the mean CFB by amplifying the signal of treatment effect and/or improving estimation precision. We evaluated different versions of PPRs and the performance of their estimators through numerical studies. A real dataset was analyzed to demonstrate the advantage of using alternative PPR over the mean CFB.

stat.ME↗

arXiv2023

Bootstrapping the Cross-Validation Estimate

Cross-validation is a widely used technique for evaluating the performance of prediction models, ranging from simple binary classification to complex precision medicine strategies. It helps correct for optimism bias in error estimates, which can be significant for models built using complex statistical learning algorithms. However, since the cross-validation estimate is a random value dependent on observed data, it is essential to accurately quantify the uncertainty associated with the estimate. This is especially important when comparing the performance of two models using cross-validation, as one must determine whether differences in estimated error are due to chance. Although various methods have been developed to make inferences on cross-validation estimates, they often have many limitations, such as requiring stringent model assumptions. This paper proposes a fast bootstrap method that quickly estimates the standard error of the cross-validation estimate and produces valid confidence intervals for a population parameter measuring average model performance. Our method overcomes the computational challenges inherent in bootstrapping a cross-validation estimate by estimating the variance component within a random-effects model. It is also as flexible as the cross-validation procedure itself. To showcase the effectiveness of our approach, we conducted comprehensive simulations and real-data analysis across two applications.

stat.ME↗

arXiv2021

Improvement of image classification by multiple optical scattering

Multiple optical scattering occurs when light propagates in a non-uniform medium. During the multiple scattering, images were distorted and the spatial information they carried became scrambled. However, the image information is not lost but presents in the form of speckle patterns (SPs). In this study, we built up an optical random scattering system based on an LCD and an RGB laser source. We found that the image classification can be improved by the help of random scattering which is considered as a feedforward neural network to extracts features from image. Along with the ridge classification deployed on computer, we achieved excellent classification accuracy higher than 94%, for a variety of data sets covering medical, agricultural, environmental protection and other fields. In addition, the proposed optical scattering system has the advantages of high speed, low power consumption, and miniaturization, which is suitable for deploying in edge computing applications.

cs.CV↗

arXiv2019

Towards More Flexible False Positive Control in Phase III Randomized Clinical Trials

Phase III randomized clinical trials play a monumentally critical role in the evaluation of new medical products. Because of the intrinsic nature of uncertainty embedded in our capability in assessing the efficacy of a medical product, interpretation of trial results relies on statistical principles to control the error of false positives below desirable level. The well-established statistical hypothesis testing procedure suffers from two major limitations, namely, the lack of flexibility in the thresholds to claim success and the lack of capability of controlling the total number of false positives that could be yielded by the large volume of trials. We propose two general theoretical frameworks based on the conventional frequentist paradigm and Bayesian perspectives, which offer realistic, flexible and effective solutions to these limitations. Our methods are based on the distribution of the effect sizes of the population of trials of interest. The estimation of this distribution is practically feasible as clinicaltrials.gov provides a centralized data repository with unbiased coverage of clinical trials. We provide a detailed development of the two frameworks with numerical results obtained for industry sponsored Phase III randomized clinical trials.

stat.ME↗

arXiv2014

A new method of peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC$\times$GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cutoffs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.

stat.AP↗