A LoRA-adapted conditional diffusion surrogate for electromagnetic calorimeter showers matches key observables within 2% RMSE and reproduces directional trends in design-utility gradients.
A unified deep learning anomaly detection and classi fication approach for smart grid environments
6 Pith papers cite this work. Polarity classification is still indexing.
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A CNN trained on time-stacked waterfall spectrograms beats non-negative matrix factorization for radioisotope identification at one false alarm per hour, but not at stricter false-alarm rates.
A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
Environmental radiation measurements combined with Geant4 simulations predict a residual background of approximately 250 events per day per kg per keV in the 10-100 eV range for NUCLEUS CaWO4 detectors, dominated by cosmic-ray neutrons after over 100x rejection.
Genetic algorithm feature selection with Extra Trees reduces PMU features from 112 to an average of 27.4 while raising macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837 on the MSU/ORNL dataset.
citing papers explorer
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Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
A LoRA-adapted conditional diffusion surrogate for electromagnetic calorimeter showers matches key observables within 2% RMSE and reproduces directional trends in design-utility gradients.
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Computer vision-based neural networks for radioisotope identification in urban environments
A CNN trained on time-stacked waterfall spectrograms beats non-negative matrix factorization for radioisotope identification at one false alarm per hour, but not at stricter false-alarm rates.
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Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability
A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.
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Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
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Particle background characterization and prediction for the NUCLEUS reactor CE$\nu$NS experiment
Environmental radiation measurements combined with Geant4 simulations predict a residual background of approximately 250 events per day per kg per keV in the 10-100 eV range for NUCLEUS CaWO4 detectors, dominated by cosmic-ray neutrons after over 100x rejection.
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Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
Genetic algorithm feature selection with Extra Trees reduces PMU features from 112 to an average of 27.4 while raising macro-F1 from 0.9118 to 0.9212 and ROC-AUC from 0.9791 to 0.9837 on the MSU/ORNL dataset.