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Yuchen Wang

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20 papers

2 author rows

AAAI Conference 2026 Conference Paper

Beyond Single Transactions: D-EMAML---Dual-Edge Motif Neural Networks for Enhanced Anti-Money Laundering Detection

Dongmei Han
Min Min
Yuchen Wang
Guoming Xu
Xiaofeng Zhou

Anti-money laundering (AML) detection is of vital importance in financial risk control. Although Graph Neural Networks (GNN) have yielded promising results, existing motif-based approaches primarily focus on node anomaly detection on simple graphs, which hinders the direct identification of anomalous edges in directed temporal transaction networks. Moreover, consecutive transaction relationships, termed dual-edge motifs, have rarely been considered in previous AML studies. To address these gaps, we propose the D-EMAML framework, which consists of: (1) Fast-Motif-Gen, a GPU-accelerated dual-edge motif graph generator with pruning; (2) D-EMGNN, an attention-enhanced heterogeneous GNN module that reduces motif-type information redundancy; (3) MELP, a label aggregation scheme projecting predictions from the motif graph to the original graph. Extensive experiments on real-world and synthetic datasets demonstrate significant improvements over representative baselines and validate the contribution of each component. To our knowledge, this is the first application of dual-edge motif graphs for GNN-based edge anomaly detection in AML.

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JBHI Journal 2026 Journal Article

Bridging the Semantic Gap: Synergistic Feature Fusion and Multi-Scale Adaptation for Medical Image Segmentation

Shaoqiang Wang
Guiling Shi
Chunxin Cheng
Xiaofeng Xu
Weixian Li
Xiangyu Gao
Yuchen Wang

Medical image segmentation is pivotal for clinical diagnosis but faces a systemic bottleneck in handling complex scenarios due to the inherent trade-offs in standard architectures. Existing methods often struggle with the “perception-representation dilemma” at the input stage and a “fusion-adaptation misalignment” during feature aggregation. To dismantle these interconnected challenges, we propose a novel Synergistic Fusion and Refinement Network (SFR-Net). Specifically, we design a Local-Regional Feature Perception (LRFP) module to couple fine-grained details with global context from the outset. To bridge the semantic gap, we introduce a Channel Refinement and Enhancement Module (CREM) as an intelligent gatekeeper in skip connections, alongside a Feature Mixing Module (FMM) to dynamically adapt to multi-scale targets at the bottleneck. Extensive experiments on four diverse datasets (CVC-ClinicDB, ISIC 2017, TN3K, and MICCAI Tooth) demonstrate that SFR-Net effectively overcomes these systemic limitations, achieving state-of-the-art performance in terms of accuracy and robustness.

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AAAI Conference 2026 Conference Paper

Dropout Prompt Learning: Towards Robust and Adaptive Vision-Language Models

Biao Chen
Lin Zuo
Mengmeng Jing
Kunbin He
Yuchen Wang

Dropout is a widely used regularization technique which improves the generalization ability of a model by randomly dropping neurons. In light of this, we propose Dropout Prompt Learning, which aims for applying dropout to improve the robustness of the vision-language models. Different from the vanilla dropout, we apply dropout on the tokens of the textual and visual branches, where we evaluate the token significance considering both intra-modal context and inter-modal alignment, enabling flexible dropout probabilities for each token. Moreover, to maintain semantic alignment for general knowledge transfer while encouraging the diverse representations that dropout introduces, we further propose residual entropy regularization. Experiments on 11 benchmarks show our method's effectiveness in challenging scenarios like low-shot learning, long-tail classification, and out-of-distribution generalization. Notably, our method surpasses regularization-based methods including KgCoOp by 5.10% and PromptSRC by 2.13% in performance on base-to-novel generalization.

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AAAI Conference 2026 Conference Paper

Modeling Trend Dynamics with Variational Neural ODEs for Information Popularity Prediction

Yuchen Wang
Dongpeng Hou
Weikai Jing
Chao Gao
Xianghua Li
Yang Liu

Predicting the future popularity of information in online social networks is a crucial yet challenging task, due to the complex spatiotemporal dynamics underlying information diffusion. Existing methods typically use structural or sequential patterns within the observation window as direct inputs for subsequent popularity prediction. However, most approaches lack the ability to explicitly model the overall trend of popularity up to the prediction time, which leads to limited predictive capability. To address these limitations, we propose VNOIP, a novel method based on variational neural Ordinary Differential Equations (ODEs) for information popularity prediction. Specifically, VNOIP introduces bidirectional jump ODEs with attention mechanisms to capture long-range dependencies and bidirectional context within cascade sequences. Furthermore, by jointly considering both cascade patterns and overall trend temporal patterns, VNOIP explicitly models the continuous-time dynamics of popularity trend trajectories with variational neural ODEs. Additionally, a knowledge distillation loss is employed to align the evolution of prior and posterior latent variables. Extensive experiments on real-world datasets demonstrate that VNOIP is highly competitive in both prediction accuracy and efficiency compared to state-of-the-art baselines.

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AAAI Conference 2026 Conference Paper

Robust Decentralized Multi-armed Bandits: From Corruption-Resilience to Byzantine-Resilience

Zicheng Hu
Yuchen Wang
Cheng Chen

Decentralized cooperative multi-agent multi-armed bandits (DeCMA2B) considers how multiple agents collaborate in a decentralized multi-armed bandit setting. Though this problem has been extensively studied in previous work, most existing methods remain susceptible to various adversarial attacks. In this paper, we first study DeCMA2B with adversarial corruption, where an adversary can corrupt reward observations of all agents with a limited corruption budget. We propose a robust algorithm, called DeMABAR, which ensures that each agent’s individual regret suffers only an additive term proportional to the corruption budget. Then we consider a more realistic scenario where the adversary can only attack a small number of agents. Our theoretical analysis shows that the DeMABAR algorithm can also almost completely eliminate the influence of adversarial attacks and is inherently robust in the Byzantine setting, where an unknown fraction of the agents can be Byzantine, i.e., may arbitrarily select arms and communicate wrong information. We also conduct numerical experiments to illustrate the robustness and effectiveness of the proposed method.

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AAAI Conference 2026 Conference Paper

Towards Real-Time Neutral Atom Array Assembly via Unsupervised Hologram Generation and Path Optimization

Ge Yan
Yuchen Wang
Junchi Yan

The rapid and reliable assembly of defect-free atom arrays poses a fundamental challenge for neutral atom quantum computing. While parallel rearrangement methods using spatial light modulators show promise, they suffer from significant overhead in two sub-tasks: atom-site matching and hologram generation. We propose a framework to address these bottlenecks and enhance the efficiency and fidelity of the assembly process. It features a new optimization objective for atom-site matching that minimizes the longest movement path, and a Fourier U-Net model that integrates Fourier operators with image-to-image translation to enable real-time hologram generation. The model is trained in a fully self-supervised paradigm, leveraging the physical properties of holography to remove the need for costly ground-truth labels. Experimental results show our framework not only significantly outperforms the state-of-the-art supervised CNN-based model but also achieves an inference speed orders of magnitude faster than traditional iterative algorithms, enabling real-time, dynamic atom rearrangement.

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ICML Conference 2025 Conference Paper

A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments

Yuchen Wang
Hongjue Zhao
Haohong Lin
Enze Xu
Lifang He 0001
Huajie Shao

This work aims to address the problem of long-term dynamic forecasting in complex environments where data are noisy and irregularly sampled. While recent studies have introduced some methods to improve prediction performance, these approaches still face a significant challenge in handling long-term extrapolation tasks under such complex scenarios. To overcome this challenge, we propose Phy-SSM, a general-purpose framework that integrates partial physics knowledge into state space models (SSMs) for long-term dynamics forecasting in complex environments. Our motivation is that SSMs can effectively capture long-range dependencies in sequential data and model continuous dynamical systems, while the incorporation of physics knowledge improves generalization ability. The key challenge lies in how to seamlessly incorporate partially known physics into SSMs. To achieve this, we decompose partially known system dynamics into known and unknown state matrices, which are integrated into a Phy-SSM unit. To further enhance long-term prediction performance, we introduce a physics state regularization term to make the estimated latent states align with system dynamics. Besides, we theoretically analyze the uniqueness of the solutions for our method. Extensive experiments on three real-world applications, including vehicle motion prediction, drone state prediction, and COVID-19 epidemiology forecasting, demonstrate the superior performance of Phy-SSM over the baselines in both long-term interpolation and extrapolation tasks. The source code will be publicly available upon publication.

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IJCAI Conference 2025 Conference Paper

A Generalized Diffusion Framework with Learnable Propagation Dynamics for Source Localization

Dongpeng Hou
Yuchen Wang
Chao Gao
Xianghua Li

Source localization has been widely studied in recent years due to its crucial role in controlling the spread of harmful information. Existing methods only achieve satisfactory performance within a specific propagation model, which restricts their applicability and generalizability across different scenarios. To address this, we propose a Generalized Diffusion Framework for Source Localization (GDFSL), which enhances probabilistic diffusion models to flexibly capture the underlying dynamics of various propagation scenarios. By redefining the forward diffusion process, GDFSL ensures convergence to a real distribution of infected states that accurately represents the targeted dynamics, enabling the model to learn unbiased noise in a self-supervised manner that encodes fine-grained propagation characteristics. A closed-form reverse diffusion process is then derived to trace the propagation back to the source. The process does not rely on an explicit source label term, facilitating direct inference of sources from observed data. Experimental results show that GDFSL outperforms SOTA methods in various propagation models, particularly in scenarios where historical training data is limited or unavailable. The code is available at https: //github. com/cgao-comp/GDFSL.

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ICLR Conference 2025 Conference Paper

Accelerating Neural ODEs: A Variational Formulation-based Approach

Hongjue Zhao
Yuchen Wang
Hairong Qi 0001
Zijie Huang 0002
Han Zhao 0002
Lui Sha
Huajie Shao

Neural Ordinary Differential Equations (Neural ODEs or NODEs) excel at modeling continuous dynamical systems from observational data, especially when the data is irregularly sampled. However, existing training methods predominantly rely on numerical ODE solvers, which are time-consuming and prone to accumulating numerical errors over time due to autoregression. In this work, we propose VF-NODE, a novel approach based on the variational formulation (VF) to accelerate the training of NODEs. Unlike existing training methods, the proposed VF-NODEs implement a series of global integrals, thus evaluating Deep Neural Network (DNN)--based vector fields only at specific observed data points. This strategy drastically reduces the number of function evaluations (NFEs). Moreover, our method eliminates the use of autoregression, thereby reducing error accumulations for modeling dynamical systems. Nevertheless, the VF loss introduces oscillatory terms into the integrals when using the Fourier basis. We incorporate Filon's method to address this issue. To further enhance the performance for noisy and incomplete data, we employ the natural cubic spline regression to estimate a closed-form approximation. We provide a fundamental analysis of how our approach minimizes computational costs. Extensive experiments demonstrate that our approach accelerates NODE training by 10 to 1000 times compared to existing NODE-based methods, while achieving higher or comparable accuracy in dynamical systems. The code is available at https://github.com/ZhaoHongjue/VF-NODE-ICLR2025.

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AIIM Journal 2025 Journal Article

DDintensity: Addressing imbalanced drug-drug interaction risk levels using pre-trained deep learning model embeddings

Weidun Xie
Xingjian Chen
Lei Huang
Zetian Zheng
Yuchen Wang
Ruoxuan Zhang
Xiao Zhang
Zhichao Liu

Imbalanced datasets have been a persistent challenge in bioinformatics, particularly in the context of drug-drug interaction (DDI) risk level datasets. Such imbalance can lead to biased models that perform poorly on underrepresented classes. To address this issue, one strategy is to construct a balanced dataset, while another involves employing more advanced features and models. In this study, we introduce a novel approach called DDintensity, which leverages pre-trained deep learning models as embedding generators combined with LSTM-attention models to address the imbalance in DDI risk level datasets. We tested embeddings from various domains, including images, graphs, and textual corpus. Among these, embeddings generated by BioGPT achieved the highest performance, with an Area Under the Curve (AUC) of 0. 97 and an Area Under the Precision-Recall curve (AUPR) of 0. 92. Our model was trained on the DDinter and further validated using the MecDDI dataset. Additionally, case studies on chemotherapeutic drugs, DB00398 (Sorafenib) and DB01204 (Mitoxantrone) used in oncology, were conducted to demonstrate the specificity and effectiveness of the this methods. Our approach demonstrates high scalability across DDI modalities, as well as the discovery of novel interactions. In summary, we introduce DDIntensity as a solution for imbalanced datasets in bioinformatics with pre-trained deep-learning embeddings.

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ICML Conference 2025 Conference Paper

Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin

Yuchen Wang
Xuefeng Bai 0001
Xiucheng Li
Weili Guan
Liqiang Nie
Xinyang Chen 0001

Adapting vision-language models (VLMs) to downstream tasks with pseudolabels has gained increasing attention. A major obstacle is that the pseudolabels generated by VLMs tend to be imbalanced, leading to inferior performance. While existing methods have explored various strategies to address this, the underlying causes of imbalance remain insufficiently investigated. To fill this gap, we delve into imbalanced pseudolabels and identify two primary contributing factors: concept mismatch and concept confusion. To mitigate these two issues, we propose a novel framework incorporating concept alignment and confusion-aware calibrated margin mechanisms. The core of our approach lies in enhancing underperforming classes and promoting balanced predictions across categories, thus mitigating imbalance. Extensive experiments on six benchmark datasets with three learning paradigms demonstrate that the proposed method effectively enhances the accuracy and balance of pseudolabels, achieving a relative improvement of 6. 29% over the SoTA method. Our code is avaliable at https: //github. com/Noahwangyuchen/CAP

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IJCAI Conference 2025 Conference Paper

Learning Neural Jump Stochastic Differential Equations with Latent Graph for Multivariate Temporal Point Processes

Yuchen Wang
Dongpeng Hou
Chao Gao
Xianghua Li

Multivariate Temporal Point Processes (MTPPs) play an important role in diverse domains such as social networks and finance for predicting event sequence data. In recent years, MTPPs based on Ordinary Differential Equations (ODEs) and Stochastic Differential Equations (SDEs) have demonstrated their strong modeling capabilities. However, these models have yet to thoroughly consider the underlying relationships among different event types to enhance their modeling capacity. Therefore, this paper introduces a method that uses neural SDEs with a jump process guided by the latent graph. Firstly, our proposed method employs multi-dimensional SDEs to capture the dynamics of the intensity function for each event type. Subsequently, a latent graph structure is integrated into the jump process without any encoder, aiming to enhance the modeling and predictive capabilities for MTPPs. Theoretical analysis guarantees the existence and uniqueness of the solution for our proposed method. The experiments conducted on multiple real-world datasets show that our approaches demonstrate significant competitiveness when compared to state-of-the-art neural point processes. Meanwhile, the trainable parameters of the latent graph also improve the model interpretability without any prior knowledge. Our code is available at https: //github. com/cgao-comp/LNJSDE.

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AAAI Conference 2025 Conference Paper

Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing

DingYi Zeng
Yuchen Wang
Honglin Cao
Wanlong Liu
Yichen Xiao
ChengzhuoLu
Wenyu Chen
Malu Zhang

Event cameras encode visual information by generating asynchronous and sparse event streams, which hold great potential for low latency and low power consumption. Despite many successful implementations of event camera-based applications, most of them accumulate the events into frames and then utilize conventional frame-based computer vision algorithms. These frame-based methods, though typically effective, diminish the inherent advantages of the event camera's low latency and low power consumption. To solve the above problems, we propose ASGCN, which efficiently processes data on an event-by-event basis and dynamically evolves into a corresponding dynamic representation, enabling low latency and high sparsity of data representation. The sparsity computation is further improved by introducing brain-inspired spiking neural networks, resulting in low power consumption for ASGCN. Extensive and diverse experiments demonstrate the energy efficiency and low latency advantages of our processing pipeline. Especially on real-world event camera datasets, our pipeline consumes more than 10,000 times less energy and achieves similar performance compared to current frame-based methods.

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EAAI Journal 2025 Journal Article

Physics-informed surrogate for cardiovascular flow extrapolation through transductive learning

Yuchen Wang
Nan Ye
Zhiyong Li

We consider learning surrogate models that directly predict cardiovascular flow fields by mapping geometry and/or fluid properties to hemodynamic parameters. Various machine learning approaches have been developed, but they generally do not extrapolate well to problems beyond the range covered by the training data. We propose a transductive physics informed neural network (T-PINN) approach to improve the extrapolation performance. Our approach builds on the standard PINN approach, which uses governing partial differential equations (PDEs) and labeled data for problems in the training regime to guide the training of neural network surrogate, but we additionally incorporate the governing PDEs for test problems from the extrapolation regimes. T-PINN demonstrates improved extrapolation performance on three synthetic cardiovascular flow problems as compared to purely data-driven neural network surrogates and standard PINNs. Additionally, we perform experiments to investigate how T-PINN’s performance varies when the physical constraints are softened, with hard boundary constraints replaced by soft ones, or simplified PDEs by full PDEs. Our results indicate that these two variants result in similar equation residuals as the original T-PINN but lead to less accurate velocity and pressure predictions. T-PINN’s enhanced extrapolation performance can be particularly significant for cardiovascular flow predictions in clinical settings, where patient morphologies and fluid properties often exhibit variations outside the collected data.

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EAAI Journal 2024 Journal Article

A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data

Yuchen Wang
C.S. Cai
Bing Han
Huibing Xie
Fengling Bao
Hanliang Wu

Bridges typically undergo regular inspections to assess their structural conditions. However, relying solely on numerical data overlooks valuable information from other data types, reducing assessment reliability. Although data fusion is an effective solution, existing methods poorly handle defective data scenarios (sparse, imbalance, and loss). To address this issue, this study proposes a novel deep learning-based assessment model, the Bridge Information Fusion Network (BI-FusionNet). The features of the developed BI-FusionNet: (1) Feature extraction and processing layers can be compatibility with processing networks for various data types, extracting and unifying the key features of these data; (2) Innovative fusion technology combining SENet and the random fusion matrix, enabling deep fusion from data types to feature information. Appropriate extraction models mitigate sparse data effects via extracting critical features; the novel feature fusion strategy exploits cross-type information, resolving the imbalanced and missing data issues. The experimental results verified that the BI-FusionNet model achieved an accuracy of 0. 9687 in assessing bridge conditions using normal dataset. In the effectiveness test, the model accuracy was 0. 8377 by using the defective dataset, outperforming baseline methods. Therefore, the proposed BI-FusionNet can alleviate the issue of performance degradation from defective data and facilitates multi-type inspection data application in bridge condition evaluation.

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IJCAI Conference 2023 Conference Paper

Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks

Yuchen Wang
Kexin Shi
Chengzhuo Lu
Yuguo Liu
Malu Zhang
Hong Qu

The brain-inspired spiking neural networks (SNNs) are receiving increasing attention due to their asynchronous event-driven characteristics and low power consumption. As attention mechanisms recently become an indispensable part of sequence dependence modeling, the combination of SNNs and attention mechanisms holds great potential for energy-efficient and high-performance computing paradigms. However, the existing works cannot benefit from both temporal-wise attention and the asynchronous characteristic of SNNs. To fully leverage the advantages of both SNNs and attention mechanisms, we propose an SNNs-based spatial-temporal self-attention (STSA) mechanism, which calculates the feature dependence across the time and space domains without destroying the asynchronous transmission properties of SNNs. To further improve the performance, we also propose a spatial-temporal relative position bias (STRPB) for STSA to consider the spatiotemporal position of spikes. Based on the STSA and STRPB, we construct a spatial-temporal spiking Transformer framework, named STS-Transformer, which is powerful and enables SNNs to work in an asynchronous event-driven manner. Extensive experiments are conducted on popular neuromorphic datasets and speech datasets, including DVS128 Gesture, CIFAR10-DVS, and Google Speech Commands, and our experimental results can outperform other state-of-the-art models.

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IJCAI Conference 2022 Conference Paper

Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion

Yuchen Wang
Malu Zhang
Yi Chen
Hong Qu

Spiking Neural Networks (SNNs) are receiving increasing attention due to their biological plausibility and the potential for ultra-low-power event-driven neuromorphic hardware implementation. Due to the complex temporal dynamics and discontinuity of spikes, training SNNs directly usually suffers from high computing resources and a long training time. As an alternative, SNN can be converted from a pre-trained artificial neural network (ANN) to bypass the difficulty in SNNs learning. However, the existing ANN-to-SNN methods neglect the inconsistency of information transmission between synchronous ANNs and asynchronous SNNs. In this work, we first analyze how the asynchronous spikes in SNNs may cause conversion errors between ANN and SNN. To address this problem, we propose a signed neuron with memory function, which enables almost no accuracy loss during the conversion process, and maintains the properties of asynchronous transmission in the converted SNNs. We further propose a new normalization method, named neuron-wise normalization, to significantly shorten the inference latency in the converted SNNs. We conduct experiments on challenging datasets including CIFAR10 (95. 44% top-1), CIFAR100 (78. 3% top-1) and ImageNet (73. 16% top-1). Experimental results demonstrate that the proposed method outperforms the state-of-the-art works in terms of accuracy and inference time. The code is available at https: //github. com/ppppps/ANN2SNNConversion_SNM_NeuronNorm.

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AAAI Conference 2021 Conference Paper

Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information

Yi Chen
Hong Qu
Malu Zhang
Yuchen Wang

Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respectively. However, how to efficiently train the DSNNs remains an open question because of the non-differentiable spike function that prevents the traditional back-propagation (BP) learning algorithm directly applied to DSNNs. Here, inspired by the findings from the biological neural networks, we address the above-mentioned problem by introducing neural oscillation and spike-phase information to DSNNs. Specifically, we propose an Oscillation Postsynaptic Potential (Os-PSP) and phase-locking active function, and further put forward a new spiking neuron model, namely Resonate Spiking Neuron (RSN). Based on the RSN, we propose a Spike-Level-Dependent Back-Propagation (SLDBP) learning algorithm for DSNNs. Experimental results show that the proposed learning algorithm resolves the problems caused by the incompatibility between the BP learning algorithm and SNNs, and achieves state-of-the-art performance in single spike-based learning algorithms. This work investigates the contribution of introducing biologically inspired mechanisms, such as neural oscillation and spike-phase information to DSNNs and providing a new perspective to design future DSNNs.

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IROS Conference 2019 Conference Paper

Unsupervised Traffic Accident Detection in First-Person Videos

Yu Yao 0006
Mingze Xu
Yuchen Wang
David J. Crandall
Ella M. Atkins

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art. Code and the dataset developed in this work are available at: https://github.com/MoonBlvd/tad-IROS2019

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ICRA Conference 2018 Conference Paper

Design, Modeling and Control of a Solar-Powered Quadcopter

Nathaniel Kingry
Logan Towers
Yen-Chen Liu
Yue Zu
Yuchen Wang
Briana Staheli
Yusuke Katagiri
Samuel Cook

This paper presents the design, modeling, control, and experimental test of a solar-powered quadcopter to allow for long-endurance missions. We first present the design of a large-scale quadcopter that incorporates solar energy harvesting capabilities. Based on the design results, we built the dynamical model of the customized quadcopter with analysis of the aerodynamic influence. A feedback control system is developed for the solar-powered quadcopter that takes into account the wind disturbance and is verified in virtual simulation examples. All parameters used in the modeling and simulations are based on a developed prototype of the solar-powered quadcopter. Flight tests with the prototype are presented to validate the feasibility and theoretical basis of the solar-powered quadcopter.

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