arXiv subjects

Zirui Fan

Publications and source records attributed to Zirui Fan.

3 recordsLinked to original sources

arXiv2026

Simulation Study of Coupling Effects Between a Hall Thruster and a Power Processing Unit

The complex and nonlinear load characteristics of Hall thrusters remain a key challenge in the design of propulsion power-supply output stages. In existing power-supply simu-lations for electric propulsion systems, the Hall thruster is often simplified as a fixed im-pedance or a prescribed current source, which makes it difficult to capture the real-time interaction between the power-supply output stage and the thruster discharge process. To address this issue, this study encapsulates a one-dimensional discharge model as an externally callable thruster slave and proposes a HallThruster.jl-Saber-Simulink co-simu-lation method. The proposed method enables synchronized closed-loop exchange be-tween the power-port voltage Vcmd and the thruster discharge current Iout . The results show that the discharge current under the co-simulation condition exhibits a sustained low-frequency response at approximately 11 kHz. Compared with a fixed-voltage standalone simulation, the co-simulation shows observable differences in port waveforms, spectral characteristics, and internal field distributions. This method provides a co-simu-lation basis for realistic load analysis of propulsion power supplies and subsequent stress evaluation of key components.

physics.plasm-ph↗

arXiv2026

BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery

Biomedical deep-research systems increasingly retrieve and synthesize scientific evidence, but their outputs typically collapse heterogeneous evidence into static text, making provenance difficult to inspect and reuse. We formulate evidence-centered biomedical knowledge discovery, where disease-associated protein signals are transformed into a structured evidence state connecting proteins, pathways, publications, interactions, claims, and uncertainty. We introduce BioInsight, a provenance-preserving multi-agent orchestration framework built around typed artifact contracts and an independent Search Agent that decouples evidence acquisition from downstream mechanistic reasoning, supporting both the citation-grounded report and an interactive evidence workspace, without independently regenerating evidence for visualization. We evaluate BioInsight on standardized biomedical QA, challenging protein-function reasoning, and end-to-end biomedical evidence synthesis. The results demonstrate that BioInsight achieves better traceability and ranking performance than standard search-augmented baselines, and suggest that biomedical AI systems should move toward provenance-preserving, interactive evidence artifacts.

cs.AI↗

arXiv2024

Uncertainty of high-dimensional genetic data prediction with polygenic risk scores

In many predictive tasks, there are a large number of true predictors with weak signals, leading to substantial uncertainties in prediction outcomes. The polygenic risk score (PRS) is an example of such a scenario, where many genetic variants are used as predictors for complex traits, each contributing only a small amount of information. Although PRS has been a standard tool in genetic predictions, its uncertainty remains largely unexplored. In this paper, we aim to establish the asymptotic normality of PRS in high-dimensional predictions without sparsity constraints. We investigate the popular marginal and ridge-type estimators in PRS applications, developing central limit theorems for both individual-level predicted values (e.g., genetically predicted human height) and cohort-level prediction accuracy measures (e.g., overall predictive $R$-squared in the testing dataset). Our results demonstrate that ignoring the prediction-induced uncertainty can lead to substantial underestimation of the true variance of PRS-based estimators, which in turn may cause overconfidence in the accuracy of confidence intervals and hypothesis testing. These findings provide key insights omitted by existing first-order asymptotic studies of high-dimensional sparsity-free predictions, which often focus solely on the point limits of predictive risks. We develop novel and flexible second-order random matrix theory results to assess the asymptotic normality of functionals with a general covariance matrix, without assuming Gaussian distributions for the data. We evaluate our theoretical results through extensive numerical analyses using real data from the UK Biobank. Our analysis underscores the importance of incorporating uncertainty assessments at both the individual and cohort levels when applying and interpreting PRS.

stat.ME↗