4 citations · 11 across the 13 of their papers we have counts for
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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…
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…
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…
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks
Ghazal Farhani, Alexander Kazachek, Boyu Wang
Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). However, they of…