Predictive Modeling of Classical and Quantum Mechanics Using Machine Learning: A Case Study with TensorFlow

• URL: http://arxiv.org/abs/2502.05621v1
• Date: Sat, 08 Feb 2025 16:02:03 GMT
• Title: Predictive Modeling of Classical and Quantum Mechanics Using Machine Learning: A Case Study with TensorFlow
• Authors: Enis Yazici,
• Abstract summary: We present several machine learning approaches for predicting the behavior of both classical and quantum systems. For the classical domain, we model a pendulum subject to multiple forces using both a standard artificial neural network (ANN) and a physics-informed neural network (PINN)
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• Abstract: In this paper, we present several machine learning approaches for predicting the behavior of both classical and quantum systems. For the classical domain, we model a pendulum subject to multiple forces using both a standard artificial neural network (ANN) and a physics-informed neural network (PINN). For the quantum domain, we predict the ground state energy of a quantum anharmonic oscillator from discretized potential data using an ANN with convolutional layers (CNN), a long short-term memory (LSTM) network, and a PINN that incorporates the Schr\"odinger equation. Detailed training outputs and comparisons are provided.

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