Neural Networks in Nonlinear Systems #worldresearchawards #researchaward #researcher #neuralnetwork
Multi-wave phenomena appear across physics, engineering, and applied mathematics—from fluid dynamics and plasma physics to optical fibers and seismic activity. Solving the complex nonlinear equations that govern these systems has traditionally required intensive analytical techniques and computational resources. Today, neural networks are transforming how scientists approach multi-wave solutions, offering faster, more flexible, and highly accurate modeling capabilities. Neural networks, particularly deep learning architectures, can approximate complex nonlinear functions and identify hidden patterns within large datasets. By training on simulated or experimental wave data, these models learn to predict wave interactions, propagation behavior, soliton formation, and stability conditions. This capability significantly reduces the time required to solve partial differential equations (PDEs) associated with multi-wave systems. Physics-informed neural networks (PINNs) take this innovation...