Computational nonlinear physics, having the integration of physics, mathematics and computer science, makes use of computational methods for the examination of nonlinear systems. Nonlinearity emerges through the collective behavior of systems, and interactions among their components which result in emergent phenomena like solitons, fractals and chaos. As the dynamics that may result in can be unpredictable, non-equilibrium pattern formation could be the outcomes. When the collective behaviors of systems cannot be simply or linearly described by adding up the effects of individual components, nonlinearity is said to occur. Computational nonlinear physics, in this regard, enable researchers to develop simulations and numerical models for understanding nonlinear and complex patterns as well as behaviors, which can be illustrated as pattern formation, turbulence and chaotic systems. In addition, means such as physics-informed neural networks (PINNs) are employed for the aim of solving nonlinear partial differential equations. To sum up, computational nonlinear physics proves to be beneficial in exploring the intricate phenomena that go beyond linear approximations, which may help with the generation of adopting new angles into natural as well as engineered systems.
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