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cs.LG2025
Bayesian Uncertainty Quantification with Anchored Ensembles for Robust EV Power Consumption Prediction
Ghazal Farhani, Taufiq Rahman, Kieran Humphries
Accurate EV power estimation underpins range prediction and energy management, yet practitioners need both point accuracy and trustworthy uncertainty. We propose an anchored-ensemb…
cs.LG2025
Deep Learning-Based Analysis of Power Consumption in Gasoline, Electric, and Hybrid Vehicles
Roksana Yahyaabadi, Ghazal Farhani, Taufiq Rahman +3
Accurate power consumption prediction is crucial for improving efficiency and reducing environmental impact, yet traditional methods relying on specialized instruments or rigid phy…
cs.LG2024
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
Nima Hosseini Dashtbayaz, Ghazal Farhani, Boyu Wang +1
The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, r…