The evolution of next-generation (NextG) mobile and wireless networks is driven by a move toward higher carrier frequencies, such as millimeter-wave (mmWave) bands. Higher frequencies provide higher capacity but also have a much shorter distance range for coverage compared to lower-frequency signals. This means that a single access point (AP) or a base station cannot cover a large area leading to smaller areas (cells) with each AP handling a smaller number of users. This dense deployment of small-cell APs necessitates a heightened level of intelligence and timely situational awareness to enhance network resilience and self-reconfigurability in the face of various challenges like network or AP failures. To tackle these challenges, this project pursues a novel resilience-native network paradigm called CatFly, which embraces a data-driven learning approach that utilizes a digital replica of the physical network with sufficient details to swiftly achieve preemptive operations against disruptions. Armed with this hybrid digital-physical (HDP) intelligence, networks are always ready and responsive, employing outcomes of their what-if analysis to reconfigure the physical network and ensure resilience. The project aims to utilize a combination of techniques such as network optimization, graph theory, machine learning, experimental measurements, and models that mix physical and virtual contexts. The project will advance networking technologies in three inter-related thrusts, fo