arXiv subjects

Saralasrita Mohanty

Publications and source records attributed to Saralasrita Mohanty.

3 recordsLinked to original sources

arXiv2026

Artificial Neural Network Assisted Modelling of Tangent Galvanometer Measurements for the Determination of Horizontal Component of Earth's Magnetic Field

The Tangent Galvanometer (TG) is a standard undergraduate laboratory experiment for estimating the horizontal component of Earth's magnetic field (BH) by measuring the angle of deflection of a magnetic needle corresponding to the current flowing through a circular coil. In this study, an artificial neural network (ANN) is used as a complementary data-driven model to predict the value of BH. A dataset comprising 225 observations obtained using 50-turn and 500-turn coils was used for developing the ANN model. After quality control, 223 observations were retained and divided into training (70%), validation (15%), and testing (15%) subsets. The model was optimized using a feed-forward neural network with Tanh activation. Three different models (Models A, B, and C) with different input variables were compared for optimum performance. Model C used five input variables: current, deflection angle, tan(theta), magnetic field produced by the coil, and number of turns. The addition of tan(theta) produced a substantial improvement in prediction performance, which was further improved by including the magnetic field produced by the coil. Model C gave the best test performance, with R2 = 0.99053, RMSE = 0.54076 microT, and MAE = 0.32940 microT. The experimental and ANN-predicted values of BH were also compared with an adopted local geomagnetic reference value of 39.0 microT. The mean experimental and ANN-predicted values were 37.38898 microT and 37.32161 microT, respectively. The results demonstrate the usefulness of ANN as a complementary tool for analyzing experimental variability and nonlinear relationships in an undergraduate physics laboratory experiment.

physics.ed-ph↗

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↗