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A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils.
Sci Rep
January 2025
School of Aerospace Engineering, Gyeongsang National University, Jinju-si, 52828, Gyeongsangnam-do, Republic of Korea.
This study introduces a novel deep learning-based technique for predicting pressure distribution images, aimed at application in image-based approximate optimal design. The proposed approach integrates both unsupervised and supervised learning paradigms, employing autoencoders (AE) for the unsupervised component and fully connected neural networks (FNN) for the supervised component. A surrogate model based on 2D image data was developed, enabling a comparative analysis of three distinct methods: the conventional AE, the convolutional autoencoder (CAE), and a hybrid CAE, which combines the CAE with a conventional AE.
View Article and Find Full Text PDFAuthor Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.
Nat Biomed Eng
January 2025
Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints.
Sci Rep
January 2025
College of computer science and technology, China University of Petroleum (East China), No.66 Changjiang West Road, Huangdao, Qingdao, 266580, Shandong, China.
Addressing the issues of inadequate information exchange among subsequences in the operational time series of water injection pumps, leading to low accuracy and high false alarm rates in anomaly detection, this paper proposes a multidimensional time series anomaly detection method for water injection pump operations, leveraging Long Short-Term Memory Autoencoder augmented with Attention Mechanism (LSTMA-AE) and mechanistic constraints. The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). The Encoder captures temporal dependencies and features within the input sequences, mapping the input data into a higher-dimensional space.
View Article and Find Full Text PDFA Network Control Theory of Dynamic Systems Approach to Personalize Therapy.
Behav Ther
January 2025
Philipps-University Marburg, Germany. Electronic address:
Contemporary latent disease models of psychopathology have shown limited clinical utility and the efficacy of conventional treatments have been disappointing. An alternative approach offers the network approach and a dynamic systems perspective to psychopathology and treatment change. To understand and modify dynamic systems, engineering and mathematics have been relying on principles of network control theory.
View Article and Find Full Text PDFModeling shock propagation and resilience in financial temporal networks.
Chaos
January 2025
Classe di Scienze, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model-a specific exponential random graph model-as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node.
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