As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously le
Deep operator networks Physics-informed neural operators Finite element modeling Static loading Elastic response Displacement and rotation 1. Introduction 1.1. Overview Over the past few decades, the finite element method (FEM) (Brenner and Scott, 2002, Hughes, 2012) has become the practical standard...
” Kumbasar explains. “People might be called somewhat tall, leading to more nuanced decisions down the line. It’s easy to build fuzzy layers in MATLAB that can be combined with neural nets or composed into complete fuzzy systems
This paper develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to...
Deep convolutional neural networks with Bee Collecting Pollen Algorithm (BCPA)-based landslide data balancing and spatial prediction. Journal of Intelligent and Fuzzy Systems, 2024, 46(1): 597–617. 110. Sepahvand, A., Beiranvand, N. Landslide susceptibility mapping using various soft computing...
Li K, Liu YS (2005) A rough set based fuzzy neural network algorithm for weather prediction. In: 2005 international conference on machine learning and cybernetics, August 2005, vol 3, pp 1888–1892. https://doi.org/10.1109/icmlc.2005.1527253 Mathur S, Kumar A, Ch M (2008) A feature bas...
Aetesam and colleagues proposed a deep CNN to remove Gaussian noise from brain MR images [30]. The method was inspired by the maximum a posteriori (MAP) with Gaussian noise and deep residual learning. Chauhan and colleagues combined a fuzzy logic approach with a CNN autoencoder to denoise ...
Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm. Sensors. 2022; 22(7):2482. https://doi.org/10.3390/s22072482 Chicago/Turabian Style Ozelim, Luan Carlos de Sena Monteiro, Lucas Parreira de Faria Borges, ...
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and enginee...
Traditional data-driven tool wear modeling methods, such as fuzzy clustering6, support vector machine7, decision tree8 and neural network9, do not require in-depth analysis of the complex tool failure mechanism in the cutting process, but are a kind of methods to predict tool wear by mining ...