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Thematische Bibliographien / Deep generative modeling / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Deep generative modeling“

Autor: Grafiati

Veröffentlicht am 22. Juni 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Blaschke, Thomas, and Jürgen Bajorath. "Compound dataset and custom code for deep generative multi-target compound design." Future Science OA 7, no. 6 (2021): FSO715. http://dx.doi.org/10.2144/fsoa-2021-0033.

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Aim: Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling. Methodology: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated. Exemplary results & data: Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. Limitations & next steps: MT-CPD design via deep learning is sti

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Joshi, Ameya, Minsu Cho, Viraj Shah, et al. "InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4377–84. http://dx.doi.org/10.1609/aaai.v34i04.5863.

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Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on seve

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Lai, Peter, and Feruza Amirkulova. "Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks." Journal of the Acoustical Society of America 151, no. 4 (2022): A253. http://dx.doi.org/10.1121/10.0011234.

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This talk presents a method for generating planar configurations of scatterers with a reduced total scattering cross section (TSCS) by means of generative modeling and deep learning. The TSCS minimization via repeated forward modeling techniques, trial-error methods, and traditional optimization methods requires considerable computer resources and time. However, similar or better results can be achieved more efficiently by training a deep learning model to generate such optimized configurations producing low scattering effect. In this work, the Conditional Wasserstein Generative Adversarial Ne

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Komanduri, Aneesh. "Toward Causal Generative Modeling: From Representation to Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29275–76. https://doi.org/10.1609/aaai.v39i28.35215.

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Deep learning has given rise to the field of representation learning, which aims to automatically extract rich semantics from data. However, there have been several challenges in the generalization capabilities of deep learning models. Recent works have highlighted beneficial properties of causal models that are desirable for learning robust models under distribution shifts. Thus, there has been a growing interest in causal representation learning for achieving generalizability in tasks involving reasoning and planning. The goal of my dissertation is to develop theoretical intuitions and pract

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Strokach, Alexey, and Philip M. Kim. "Deep generative modeling for protein design." Current Opinion in Structural Biology 72 (February 2022): 226–36. http://dx.doi.org/10.1016/j.sbi.2021.11.008.

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6

Tomczak, Jakub M. "Deep Generative Modeling: From Probabilistic Framework to Generative AI." Entropy 27, no. 3 (2025): 238. https://doi.org/10.3390/e27030238.

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7

Lopez, Romain, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, and Nir Yosef. "Deep generative modeling for single-cell transcriptomics." Nature Methods 15, no. 12 (2018): 1053–58. http://dx.doi.org/10.1038/s41592-018-0229-2.

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8

Lee, Ung-Gi, Sang-Hee Kang, Jong-Chan Lee, Seo-Yeon Choi, Ukmyung Choi, and Cheol-Il Lim. "Development of Deep Learning-based Art Learning Support Tool: Using Generative Modeling." Korean Association for Educational Information and Media 26, no. 1 (2020): 207–36. http://dx.doi.org/10.15833/kafeiam.26.1.207.

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9

Behnia, Farnaz, Dominik Karbowski, and Vadim Sokolov. "Deep generative models for vehicle speed trajectories." Applied Stochastic Models in Business and Industry 39, no. 5 (2023): 701–19. http://dx.doi.org/10.1002/asmb.2816.

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AbstractGenerating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self‐driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed‐forward layers and are trained using advers

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Janson, Giacomo, and Michael Feig. "Transferable deep generative modeling of intrinsically disordered protein conformations." PLOS Computational Biology 20, no. 5 (2024): e1012144. http://dx.doi.org/10.1371/journal.pcbi.1012144.

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Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such

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Zhang, Chun, Liangxu Xie, Xiaohua Lu, Rongzhi Mao, Lei Xu, and Xiaojun Xu. "Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery." Molecules 29, no. 7 (2024): 1499. http://dx.doi.org/10.3390/molecules29071499.

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Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirection

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Akshansh, Mishra, and Pathak Tarushi. "Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy." Indian Journal of Data Mining (IJDM) 1, no. 1 (2021): 1–6. https://doi.org/10.54105/ijdm.A1603.051121.

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Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain im

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Guliev, R. "Generative adversarial networks for modeling reservoirs with permeability anisotropy." IOP Conference Series: Materials Science and Engineering 1201, no. 1 (2021): 012066. http://dx.doi.org/10.1088/1757-899x/1201/1/012066.

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Abstract The geological model is a main element in describing the characteristics of hydrocarbon reservoirs. These models are usually obtained using geostatistical modeling techniques. Recently, methods based on deep learning algorithms have begun to be applied as a generator of a geologic models. However, there are still problems with how to assimilate dynamic data to the model. The goal of this work was to develop a deep learning algorithm - generative adversarial network (GAN) and demonstrate the process of generating a synthetic geological model: • Without integrating permeability data int

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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent sys

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Drygala, C., B. Winhart, F. di Mare, and H. Gottschalk. "Generative modeling of turbulence." Physics of Fluids 34, no. 3 (2022): 035114. http://dx.doi.org/10.1063/5.0082562.

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We present a mathematically well-founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots from the invariant measure of the chaotic system. Based on this analysis, we study a hierarchy of chaotic systems starting with the Lorenz attractor and then carry on to the modeling of turbulent flows with GAN. As training data, we use fields of velocity fluctuations obtained from large-eddy

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Qiu, Cheng, Anam Abbas, and Feruza Amirkulova. "Pentamode metamaterial design via generative modeling and deep learning." Journal of the Acoustical Society of America 151, no. 4 (2022): A255. http://dx.doi.org/10.1121/10.0011241.

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In this talk, the deep learning-assisted models will be presented for the design of pentamode unit cells to construct a 3-D lattice structure that can mimic the acoustic properties of water. The pentamode models were implemented and modified by altering the properties of the structure during the full-wave simulation performed on COMSOL Multiphysics software to ensure they meet the requirements of specific appliances for manufacture. The design is further improved by the inverse design technique. The implementation of conditional Wasserstein Generative Adversarial Networks with gradient penalty

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Veres, Matthew, Medhat Moussa, and Graham W. Taylor. "Modeling Grasp Motor Imagery Through Deep Conditional Generative Models." IEEE Robotics and Automation Letters 2, no. 2 (2017): 757–64. http://dx.doi.org/10.1109/lra.2017.2651945.

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Zhang, Zaiwei, Zhenpei Yang, Chongyang Ma, et al. "Deep Generative Modeling for Scene Synthesis via Hybrid Representations." ACM Transactions on Graphics 39, no. 2 (2020): 1–21. http://dx.doi.org/10.1145/3381866.

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Wang, Yong, Guoliang Li, Kaiyu Li, and Haitao Yuan. "A Deep Generative Model for Trajectory Modeling and Utilization." Proceedings of the VLDB Endowment 16, no. 4 (2022): 973–85. http://dx.doi.org/10.14778/3574245.3574277.

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Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications, e.g. , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks ( e.g. , popularity ranking), rather than acquiring and pro

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Martínez-Palomera, Jorge, Joshua S. Bloom, and Ellianna S. Abrahams. "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters." Astronomical Journal 164, no. 6 (2022): 263. http://dx.doi.org/10.3847/1538-3881/ac9b3f.

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Abstract The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven model

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Amirkulova, Feruza, Linwei Zhou, Anam Abbas, Peter Lai, Cheng Qiu, and Tristan A. Shah. "Acoustic metamaterial design framework using deep learning and generative modeling." Journal of the Acoustical Society of America 151, no. 4 (2022): A253. http://dx.doi.org/10.1121/10.0011233.

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This talk presents our findings and research performed on the application of deep learning and generative modeling in acoustic metamaterial design. Specifically, we will discuss our research findings published in recent papers on the application of deep learning algorithms, generative neural networks, reinforcement learning models, and global optimization for the inverse design of 2-D and 3-D acoustic metamaterial structures. The examples will be shown for the implementation of neural networks models for the inverse design of 3-D pentamode structures resulting in a low shear modulus, high bulk

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Tong, Haojing. "A deep dive into generative modeling: Evaluating DF-GANs, DM-GANs, and AttnGAN." Applied and Computational Engineering 55, no. 1 (2024): 1–7. http://dx.doi.org/10.54254/2755-2721/55/20241099.

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Generative Adversarial Networks (GANs) have become pivotal for generating synthetic data. This paper conducts a comprehensive comparison of three cutting-edge GAN models. In particular, this study delved deep into the architectural intricacies, strengths, and limitations of each model, emphasizing their distinct features and mechanisms. DF-GANs focus on producing natural images with a single-stage backbone, DM-GANs leverage memory structures to enhance model performance, while AttnGAN employs attention-driven, multi-stage refinement for precise text-to-image generation. Through a series of lit

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Korotka, Larysa, and Viktor Makarchenko. "Application of generative-adversarial networks in modeling plasma-chemical processes for obtaining nanosystems." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling" 1, no. 1 (13) (2025): 7–21. https://doi.org/10.20998/2411-0558.2025.01.01.

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The use of neural networks makes it possible to take into account the nonlinearity and complex relationships between the parameters of the technological process, which is multifactorial, with dependencies between the parameters that are difficult to formalize by classical methods. To generate additional data in modeling physiochemical processes for the production of nanosystems, it is proposed that generative adversarial networks (GANs) be used. The problematic aspects of the process of obtaining such training arrays are considered, in particular: the quality of synthetic data, preservation of

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Han, Zhiyu. "A Review on Background, Technology, Comparison, and Future Tendency of Video Generation." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 42–49. https://doi.org/10.54097/18313836.

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Video generation techniques incorporate recent advances in deep learning and generative modeling, and are widely used in film and television, education, advertising, virtual reality, and other fields. The background lies in the growing need to generate high-resolution, dynamically consistent, and semantically accurate videos to meet diverse scene requirements. Existing techniques, including Generative Adversarial Networks (GANs), Variational Auto-Encoders (VAEs), Transformer and Diffusion Models, have achieved significant improvements in video quality and generation efficiency. This paper syst

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S. Manoha. "A Deep Dive into Training Algorithms for Deep Belief Networks." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 178–86. https://doi.org/10.52783/jisem.v10i13s.2021.

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Deep Belief Networks (DBNs) have emerged as powerful tools for feature learning, representation, and generative modeling. This paper presents a comprehensive exploration of the various training algorithms employed in the training of DBNs. DBNs, composed of multiple layers of stochastic hidden units, have found applications in diverse domains such as computer vision, natural language processing, and bioinformatics. The paper begins by delving into the pre-training phase, where Restricted Boltzmann Machines (RBMs) play a central role. We review the Contrastive Divergence (CD) and Persistent Cont

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MATSUMOTO, Yuma, Taro YAOYAMA, Sangwon LEE, Takenori HIDA, and Tatsuya ITOI. "Probabilistic Three-Component Ground Motion Time History Generation Modeling Using Deep Generative Model." Journal of Japan Association for Earthquake Engineering 24, no. 4 (2024): 4_12–4_25. http://dx.doi.org/10.5610/jaee.24.4_12.

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Wang, Wei-Ching. "Sound localization via deep learning, generative modeling, and global optimization." Journal of the Acoustical Society of America 151, no. 4 (2022): A255. http://dx.doi.org/10.1121/10.0011240.

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An acoustic lens is capable of focusing incident plane waves at the focal point. This talk will discuss a new approach to design metamaterials with a focusing effect using effective and innovative methods by using machine learning. Specifically, the physics simulations use multiple scattering theory and machine learning techniques such as deep learning and generative modeling. The 2-D-Global Optimization Networks (2-D-GLOnets) model [1] developed initially for acoustic cloak design is adapted and generalized to design and optimize the acoustic lens. We supply the absolute pressure amplitude an

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Xu, Haowen, Sisi Zlatanova, Ruiyu Liang, and Ismet Canbulat. "Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning." Fire 8, no. 8 (2025): 293. https://doi.org/10.3390/fire8080293.

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Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative artificial intelligence (AI) models—such as GANs, VAEs, and transformers—can serve as transformative tools for wildfire prediction and simulation. These

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Chai, Kaitian. "Combining Deep Generative Models with Generalized Linear Models for Image Generation and Repair Systems: Transitioning from Statistical Modeling to Deep Learning." Applied and Computational Engineering 161, no. 1 (2025): 24–29. https://doi.org/10.54254/2755-2721/2025.kl24082.

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This study proposes a novel hybrid framework that integrates deep generative models and generalized linear models. Considering the limitation that generative models such as GAN and VAE can create realistic images but lack interpretation, we combine the statistical modeling capability of GLM with the abstract representation of deep learning by sharing the latent space. In the model architecture, the GLM branch ensures the consistency of the image structure, and the generative network is responsible for reconstructing semantic features. The two work collaboratively. The three types of random mis

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Borysov, Stanislav S., Jeppe Rich, and Francisco C. Pereira. "How to generate micro-agents? A deep generative modeling approach to population synthesis." Transportation Research Part C: Emerging Technologies 106 (September 2019): 73–97. http://dx.doi.org/10.1016/j.trc.2019.07.006.

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Li, Shuai, and Hongjun Li. "Deep Generative Modeling Based on VAE-GAN for 3D Indoor Scene Synthesis." International Journal of Computer Games Technology 2023 (September 20, 2023): 1–11. http://dx.doi.org/10.1155/2023/3368647.

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With the advancement of virtual reality and 3D game technology, the demand for high-quality 3D indoor scene generation has surged. Addressing this need, this paper presents a method leveraging a VAE-GAN-based framework to conquer two primary challenges in 3D scene representation and deep generative networks. First, we introduce a matrix representation to encode fine-grained object attributes, alongside a complete graph to implicitly capture object spatial relations—effectively encapsulating both local and global scene structures. Second, we devise a unique generative framework based on VAE-GAN

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Yuan, Hao, Lei Cai, Zhengyang Wang, Xia Hu, Shaoting Zhang, and Shuiwang Ji. "Computational modeling of cellular structures using conditional deep generative networks." Bioinformatics 35, no. 12 (2018): 2141–49. http://dx.doi.org/10.1093/bioinformatics/bty923.

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Abstract Motivation Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures. Results We formulate such a task as a conditional image generation problem and propose to use conditional generative adversarial networks for tackling it. We employ an encoder–decoder network

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Yang, Jingrong. "A Survey on Hair Modeling." Highlights in Science, Engineering and Technology 115 (October 28, 2024): 512–26. http://dx.doi.org/10.54097/y7bzrg65.

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Hair modeling is a vital research area in computer graphics, aiming to create realistic, controllable, and interactive hair models. Recent advancements in computer vision, deep learning, and physical simulations have significantly propelled the field of hair modeling. This paper provides a comprehensive overview of current hair modeling techniques, including generative models, physics-based approaches, interactive editing methods, deep learning-driven methods, and 3D modeling and reconstruction techniques. The fundamental principles, advantages, limitations, and potential applications of these

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Johnsen, Martin, Oliver Brandt, Sergio Garrido, and Francisco Pereira. "Population synthesis for urban resident modeling using deep generative models." Neural Computing and Applications 34, no. 6 (2021): 4677–92. http://dx.doi.org/10.1007/s00521-021-06622-2.

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Borsoi, Ricardo Augusto, Tales Imbiriba, and Jose Carlos Moreira Bermudez. "Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing." IEEE Transactions on Computational Imaging 6 (2020): 374–84. http://dx.doi.org/10.1109/tci.2019.2948726.

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Zhang, Qing, Benqiang Wang, Xusheng Liang, Yizhen Li, Feng He, and Yuexiang Hao. "Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks." Geofluids 2022 (September 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/9159242.

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Accurately establishing a 3D digital core model is of great significance in oil and gas production. The physical experiment method and numerical modeling method are common modeling methods. With the development of deep learning technology, a variety of deep learning algorithms have been applied to digital core modeling. The digital core modeling method based on generative adversarial neural networks (GANs) has attracted wide attention due to its good quality and simple generation process. The disadvantage of this method is that the network needs thousands of trainings to achieve acceptable res

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Faez, Faezeh, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah, and Hamid R. Rabiee. "SCGG: A deep structure-conditioned graph generative model." PLOS ONE 17, no. 11 (2022): e0277887. http://dx.doi.org/10.1371/journal.pone.0277887.

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Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new

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Mishra, Akshansh, and Tarushi Pathak. "Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy." Indian Journal of Data Mining 1, no. 1 (2021): 1–6. http://dx.doi.org/10.35940/ijdm.a1603.051121.

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Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain im

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Mishra, Akshansh, and Tarushi Pathak. "Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy." Indian Journal of Data Mining 1, no. 1 (2021): 1–6. http://dx.doi.org/10.54105/ijdm.a1603.051121.

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Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain im

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Eguchi, Raphael R., Christian A. Choe, and Po-Ssu Huang. "Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation." PLOS Computational Biology 18, no. 6 (2022): e1010271. http://dx.doi.org/10.1371/journal.pcbi.1010271.

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While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used wit

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Bucher, Martin Juan José, Michael Anton Kraus, Romana Rust, and Siyu Tang. "Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models." Automation in Construction 156 (December 2023): 105128. http://dx.doi.org/10.1016/j.autcon.2023.105128.

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42

Zia, Rabbia, Mariam Rehman, Afzaal Hussain, Shahbaz Nazeer, and Maria Anjum. "Improving synthetic media generation and detection using generative adversarial networks." PeerJ Computer Science 10 (September 20, 2024): e2181. http://dx.doi.org/10.7717/peerj-cs.2181.

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Synthetic images are created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media regulations and spread false information. To address these concerns, the study proposed an improved generative adversarial network (GAN) model which improves accuracy while differentiating between real and fake images focusing on data augmentation and label smoothing strategies for GAN training. The study utilizes a dataset containing human fa

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Bianco, Michael J., Sharon Gannot, Efren Fernandez-Grande, and Peter Gerstoft. "Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling." IEEE Access 9 (2021): 84956–70. http://dx.doi.org/10.1109/access.2021.3087697.

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Bianco, Michael J., Sharon Gannot, Efren Fernandez-Grande, and Peter Gerstoft. "Semi-supervised source localization in reverberant environments using deep generative modeling." Journal of the Acoustical Society of America 148, no. 4 (2020): 2662. http://dx.doi.org/10.1121/1.5147419.

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45

Masoumi, Amin, and Mert Korkali. "Adversarially robust power grid resource adequacy estimation with deep generative modeling." Electric Power Systems Research 241 (April 2025): 111374. https://doi.org/10.1016/j.epsr.2024.111374.

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Dai, Jincheng, Xiaoqi Qin, Sixian Wang, Lexi Xu, Kai Niu, and Ping Zhang. "Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency." IEEE Wireless Communications 31, no. 4 (2024): 48–56. http://dx.doi.org/10.1109/mwc.005.2300574.

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Jaroslawski, Tomek, Aakash Patil, and Beverley J. McKeon. "Predicting turbulence structure in street-canyon flows using deep generative modeling." International Journal of Heat and Fluid Flow 115 (September 2025): 109849. https://doi.org/10.1016/j.ijheatfluidflow.2025.109849.

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Yoshimori, Atsushi, Filip Miljković, and Jürgen Bajorath. "Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling." Molecules 27, no. 2 (2022): 570. http://dx.doi.org/10.3390/molecules27020570.

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Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational ap

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Pham, Tuan Minh, and Xiangyang Ju. "Simulation of Hadronic Interactions with Deep Generative Models." EPJ Web of Conferences 295 (2024): 09034. http://dx.doi.org/10.1051/epjconf/202429509034.

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Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadronic interactions. Unfortunately, the absence of first-principle theoretical guidance has made this a formidable challenge. The state-of-the-art simulation tool, Geant4, currently relies on phenomenology-inspired parametric models. Each model is designed to simulate hadronic interactions within specific energy ranges and for particular types of hadrons. Despite dedicated tuning efforts, these models sometimes fail to de

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Huang, Wenlu, Mizanur Pranto, and Jianguo Yan. "Seismic Impedance Inversion Based on Improved Cycle-WGAN." Journal of Physics: Conference Series 2868, no. 1 (2024): 012033. http://dx.doi.org/10.1088/1742-6596/2868/1/012033.

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Abstract The application of deep learning methods for seismic impedance inversion usually requires a large amount of labeled data to train the network, while labeled data available in practical applications is often limited, which affects the effectiveness of the relevant methods. In order to address this problem, this paper proposes one kind of deep learning method of a closed-loop cycle Wasserstein generative adversarial network (Cycle-WGAN) for seismic impedance inversion based on the combination of “data-driven and model-driven”. The method uses a small amount of labeled data and unlabeled

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