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ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen +1 more
wiley +1 more source
Grey wolf optimization for sensory-driven fortification of biscuits with moringa leaf and jackfruit seed powders: balancing processing and consumer acceptance. [PDF]
Kumar D +10 more
europepmc +1 more source
Comparative Evaluation of Machine Learning and Conventional Material Decomposition Algorithms for Spectral Chest Radiography Using a CdTe Photon-Counting Detector. [PDF]
Marupudi S, Ghammraoui B.
europepmc +1 more source
An Improved IHBA-BP Neural Network for Temperature Compensation of Load Cells. [PDF]
Zhang ZJ, Cheng WS, Zhang DX.
europepmc +1 more source
Optimization of Moving Cone Liner Dynamics and Health Status Prediction for Cone Crushers. [PDF]
Li M, Fu R, Wu D, Zhao L.
europepmc +1 more source
High-Precision MEMS Resonant Pressure Sensor for Real-Time Barometric Monitoring. [PDF]
Xia F +10 more
europepmc +1 more source
The Ground-Set-Cost Budgeted Maximum Coverage Problem. [PDF]
van Heuven van Staereling I +2 more
europepmc +1 more source
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Pooling Problems with Polynomial-Time Algorithms
Journal of Optimization Theory and Applications, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Haugland, Dag, Hendrix, Eligius M.T.
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