## 1 Introduction  
Light-matter interactions at the nanoscale are primarily dictated by the strength and spatial distribution of the electromagnetic fields inside nanophotonic structures. The ability to achieve a designable electromagnetic field distribution inside a photonic structure is vital when designing photonic devices for a wide variety of applications, ranging from sensing, optoelectronics, and nonlinear optics to quantum communications. The inverse design of a photonic structure with a desired field distribution requires physics-inspired and intuition-based approaches involving solving Maxwell equations [1]. Remarkable success has been demonstrated using these traditional techniques; however, given the increasing complexity of nanophotonic devices and advancements in nanofabrication, such trial-and-error approaches are now becoming computationally expensive and time consuming. Recently, a growing interest in exploring new optimization-based and data-driven-based approaches to inverse design nanophotonic structures has emerged [2]. The main optimization-based approaches that have been utilized so far in nanophotonics include evolutionary methods, such as genetic algorithms and particle swarming [3; 4; 5], simulated annealing [6], and gradient-based methods [7; 8; 9; 10]. For data-driven approaches, machine learning techniques have been primarily used for the inverse design of nanophotonic structures. A general advantage of using data-driven methods over optimization-based approaches is that in data-driven approaches patterns can be learned even in very diverse datasets and a solution can be found extremely quickly after the learning phase. This behavior is advantageous for the simultaneous design of nanophotonic devices for different applications, such as designing a sensor for multiple wavelengths for which a common database can be used for training. Although not extensively investigated, another advantage of data-driven approaches is that they allow for the classification of different nanophotonic structures based on their electromagnetic field distributions or other properties. To date, machine learning has primarily been utilized to establish mappings between geometric parameters of nanophotonic structures and the reflection/transmission/absorption spectra [11; 12; 13; 14; 15]. However, the actual electromagnetic fields inside the structures are also of great importance, as they dictate the strength of light-matter interactions. For example, in nonlinear optical media, the nonlinear overlap integral, which dictates the nonlinear wave mixing, depends on the field distribution. Similarly, for sensing applications, in addition to the Purcell enhancement, the field distribution is crucial in determining the overall sensitivity of the device. The classification of nanophotonic structures based on their electromagnetic field distributions or intensity distributions is a vital preliminary task in the inverse design of nanophotonic structures. In this preliminary stage, classification allows for reduction of the investigated application-specific parameter space and can provide a new and efficient way of extracting information from complex datasets, especially those characteristics that may be difficult to extract purely from visualization-based analysis. In this paper, we address this classification based on field distributions of nanophotonic structures and demonstrate, for the first time, the classification of high transmission eigenchannels of two-dimensional disordered systems using spatial intensity distributions. Disordered systems were chosen for this study because the speckle-like intensity distributions are extremely complex with large random spatial fluctuations that arise from the interference between the multiply scattered waves. Such systems are excellent candidates for this study, as a classification based purely on visualization is not possible.