Wave Dynamics in Cold Plasma: A FDTD and Machine Learning Framework for Wave Propagation and Forecasting
Authors
Dr. Bheem Singh Jatav
Department of Physics, M.S.H.K.P.S. Government College Revdar, Rajasthan, IndiaArjun Singh Vijoriya
Mechatronics Engineering Department, Parul University, Vadodara, Gujarat, India
Publication Details
Journal: Vijoriya International Journal for Research & Innovation
ISSN: 3107-9806
Volume: 1 Issue: 1
Year: July–December 2025
Published on: September 6, 2025
Pages: 1–12
DOI: https://doi.org/10.65595/LXWL9400
Abstract
We present a novel hybrid computational framework that couples classical finite-difference time-domain (FDTD) simulation with machine learning (ML) to model and forecast nonlinear wave propagation in a cold plasma. In this study, a one-dimensional (1D) FDTD Maxwell–Drude solver is implemented in Google Colab to simulate electromagnetic waves in a cold (collisionless) plasma. The governing equations (Maxwell’s curl equations coupled to the cold-plasma polarization equation) are solved with absorbing boundary conditions. To extract nonlinear features (such as wave steepening) and to predict future wave profiles, we employ an autoencoder to compress the simulated field snapshots into a low-dimensional latent space, and a shallow neural network to forecast the wave evolution in that latent space. Quantitative results are presented, including the dispersion relation versus plasma frequency and time-domain field amplitude growth. We demonstrate that the ML model accurately reproduces the FDTD wave envelopes and can forecast beyond the simulation horizon. Compared to pure simulation methods, our hybrid pipeline offers interpretable latent variables and rapid extrapolation capabilities, making it a novel and impactful tool for plasma wave dynamics and forecasting.
Keywords
Cold plasma, FDTD, Maxwell–Drude solver, machine learning, autoencoder, nonlinear wave propagation, forecasting, electromagnetic simulation