arXiv subjects

Prabhu Prasad Tripathy

Publications and source records attributed to Prabhu Prasad Tripathy.

2 recordsLinked to original sources

arXiv2026

Engineering edge states in two-leg SSH ladder and their topoelectric circuit realization

We study the topological phase transition in a two-leg Su-Schrieffer-Heeger (SSH) ladder by redefining the unit-cell structure. For both identical hopping dimerization pattern (uniform) and alternate hopping dimerization pattern (staggered) along the legs of the ladder, we demonstrate that different unit-cell choices generate different topological phases and phase transitions. In the uniformly dimerized ladder, variation of the inter-leg coupling induces a transition from topological phase to another topological phase through a gapless region. In contrast, the staggered dimerization configuration exhibits a richer phase structure, supporting both topological-topological and trivial-topological transitions occurring through a single gap-closing point, depending on the unit-cell definition. The phases are characterized through bulk-boundary correspondence, edge-state analysis, and bulk topological invariants. Interestingly, we obtain that while all the topological phases host two zero energy edge states each, the topological phase for the staggerred dimerization case at small inter-leg coupling hosts four edge states. We then perform topoelectric circuit simulation and experiments to observe the signatures of the topological phases. By using circuit impedance and voltage responses we establish the emergence of distinct edge modes in the circuit. Our analysis provides a route to engineer topological edge modes in a two-leg SSH ladder set-up.

cond-mat.other↗

arXiv2026

Integrating Artificial Neural Networks into Undergraduate Physics Laboratory: A Compound Pendulum Case Study

Artificial Neural Networks (ANNs) are becoming important tools in physics research and education because they help in data analysis and complement traditional analytical methods. In this work, ANN modeling is introduced in a standard compound pendulum experiment used to determine the acceleration due to gravity, g. The aim is not to replace the conventional analytical method, but to demonstrate how machine learning can support experimental data analysis in undergraduate physics laboratories. Students first measure parameters such as effective length, time period, and angular displacement, and determine g using standard analytical methods with uncertainty analysis. These experimentally obtained data are then used to train and test an ANN model. The dataset is divided into training (70%), validation (15%), and testing (15%) groups. The experimentally determined value of gravitational acceleration was 1009.03 +/- 6.82 cm/s^2, while the ANN predicted a mean value of 1009.029858 cm/s^2 with a mean absolute error of 0.000592 cm/s^2. The close agreement between the experimental and ANN-predicted values shows that the ANN successfully learned the relationship between the pendulum parameters and g. However, the ANN prediction error should not be considered as an improvement in experimental accuracy because the model is trained using experimentally derived data. Instead, the ANN serves as a useful computational and educational tool that introduces students to regression, validation, overfitting, and data-driven analysis alongside traditional experimental physics.

physics.ed-ph↗