Nishchal Dwivedi's scientific contributions

This research paper focuses on the implementation of radial Basis Function (RBF) Support Vector Machines (SVM) for classifying asteroid orbits. Asteroids are important astronomical objects, and their orbits play a crucial role in understanding the dynamics of the solar system. The International Astronomical Union maintains data archives that provide a playground to experiment with various machine-learning techniques. In this study, we explore the application of RBF SVM algorithm to classify asteroids. The results show that the RBF SVM algorithm provides a good efficiency and accuracy to the dataset. We also analyze the impact of various parameters on the performance of the RBF SVM algorithm and present the optimal parameter settings. Our study highlights the importance of using machine learning techniques for classifying asteroid orbits and the effectiveness of the RBF SVM algorithm in this regard.

Statistical physics is important in understanding the physics of interacting many bodies. This has been historically developed by attempts to understand colliding gases and quantifying quantities like entropy, free energy, and other thermodynamic quantities. An important contribution in statistical physics was by Boltzmann in the form of the H-theorem, which considered collisions between particles and used the assumption of molecular chaos or Stosszahlansatz to understand macroscopic irreversibility. To elucidate these ideas, Mark Kac introduced a classical analog called Kac rings. In this work, we attempt to introduce quantum-ness in a Kac ring and study its entropy and recurrence, comparing and contrasting to corresponding trends in a classical Kac ring. We look at the trends of recurrence time for a system with a qubit as a pointer. We further study the time distribution of entropy for these systems.