Nicolosi publishes research on Fourier-based neural networks
Posted by Kimber Crull
Dr. Gabriel Nicolosi, assistant professor of engineering management and systems engineering, has published new research in Transactions on Machine Learning Research (TMLR). The paper, titled “Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning,” was co-authored by Mominul Rubel, a Ph.D. student in engineering management.
The study introduces the Fourier Learning Machine (FLM), a neural network architecture developed in Nicolosi’s lab. The approach uses a nonharmonic Fourier series to address complex partial differential equations and optimal control problems with improved adaptability compared to many established models.
Posted by Kimber Crull
