Plasma-chemical interactions in liquids and at gas-liquid interfaces provide a means for producing reactive species in the liquid – processes that are important to environmental remediation, water purification, biotechnology, and agriculture. Much research has been focused on gaining a more detailed understanding of the different processes occurring at the plasma-liquid interface. Despite numerous experimental and numerical studies on this topic, there remains a lack of knowledge of the physicochemical processes underpinning several near-interfacial processes, particularly self-organization. This project will develop multidimensional multi-physics models capable of handling interfacial surface dynamics/instability in the presence of electrical forces, plasma-induced liquid phase flows, heating of the liquid, and associated phase change (i.e., evaporation or vaporization depending on the temperature) due to plasma interaction with liquid, the transport of charged species into the liquid phase all of which are crucial elements to obtain a fundamental understanding of the self-organized plasma pattern formation in liquid interface. A novel physics-informed neural network (PINN) model for interfacial two-phase flows will be developed to reduce computational time and conduct high throughput calculations. Canonical experiments with sophisticated diagnostics will be performed to measure the evolution of capillary wave patterns, self-organized pattern morphology, changes in the liquid surface conductivity, and electrical field. In addition to providing experimental insight, these data will serve as model validation targets.