7 citations · 7 across the 3 of their papers we have counts for
5 papers
Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing
Abhijit Sen, Bikram Keshari Parida, Shital Chauhan +2
Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and expensive to simulate as system s…
Geometric Kolmogorov--Arnold Network (GeoKAN)
Abhijit Sen, Bikram Keshari Parida, Giridas Maiti +2
We introduce Geometric Kolmogorov--Arnold Networks (GeoKANs), a family of geometry-aware KAN-type models in which approximation is carried out in learned, geometry-adapted coordina…
From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial
Abhijit Sen, Sonali Panda, Mahima Arya +3
This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the g…
Feature Engineering is Not Dead: Reviving Classical Machine Learning with Entropy, HOG, and LBP Feature Fusion for Image Classification
Abhijit Sen, Giridas Maiti, Bikram K. Parida +3
Feature engineering continues to play a critical role in image classification, particularly when interpretability and computational efficiency are prioritized over deep learning mo…
Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN
Subhajit Patra, Sonali Panda, Bikram Keshari Parida +4
Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However,…