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Found 8 papers, 4 papers with code
Bayesian optimized physics-informed neural network for estimating wave propagation velocities
The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities.
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support
In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT based environmental control system that integrates sensor technology and advanced machine learning decision support.
Deep generative models for unsupervised delamination detection using guided waves
With the rising demands for robust structural health monitoring procedures for aerospace structures, the scope of intelligent algorithms and learning techniques is expanding.
Towards deep generation of guided wave representations for composite materials
The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material.
Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search
In this paper, leakage estimation in deep space habitats is posed as an inverse problem.
Computing Anti-Derivatives using Deep Neural Networks
We have used this algorithm to obtain the anti-derivatives of several functions, including non-elementary and oscillatory integrals.
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i. e., finding layup sequence type and identifying material properties.
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals.
