CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A scoping review of physics-informed machine learning for seismic wave propagation finds applications in forward and inverse problems with often comparable accuracy at lower cost, while identifying gaps in benchmarking, training cost, and 3D/experimental validation.
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Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
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A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
A scoping review of physics-informed machine learning for seismic wave propagation finds applications in forward and inverse problems with often comparable accuracy at lower cost, while identifying gaps in benchmarking, training cost, and 3D/experimental validation.