Thermal Fluctuation

Thermal fluctuation research focuses on understanding and modeling the impact of random energy variations on diverse systems, from combustion processes to neural network training. Current investigations employ machine learning techniques, including decision trees, convolutional neural networks, and Fourier neural operators, to analyze complex datasets and predict system behavior in the presence of noise, particularly in scenarios with strong thermal fluctuations or hybrid instability mechanisms. These studies are crucial for improving the accuracy of parameter estimation in various fields and for developing more robust models of complex physical phenomena, ultimately leading to advancements in areas like combustion efficiency and AI-driven scientific discovery.