Significant experimental efforts have been devoted to studying (anti)neutrino-nucleus interactions [1, 2] in the energy range relevant for next-generation neutrino oscillation experiments, such as Hyper-Kamiokande [3] and DUNE [4]. In parallel, theoretical models describing these interactions have been developed [2]. The outcomes of both experimental and theoretical advances are incorporated into Monte Carlo (MC) event generators, which simulate (anti)neutrino-nucleus collisions under realistic conditions [5-10]. MC generators are often tuned to reproduce experimental observations, relying on adjustable parameters that are fitted using available data [11]. However, this tuning process cannot fully compensate for the fundamental limitations of the underlying models, especially those relying on complex approximations, such as nuclear modeling. Consequently, there is a growing interest in alternative approaches to traditional MC event generation--methods that can learn directly from experimental data and dynamically refine their predictions.

The first type of 13 interaction plays a pivotal role in the oscillation analyses carried out by the T2K and Hyper-Kamiokande experiments, and the other is important for the DUNE experiment. We consider various kinematic distributions of the charged lepton. The models we present successfully reproduce the peak structure in data distributions. Once these models are developed, they generate events significantly faster than "classical" generators. We also anticipate that these models can be adapted to more realistic scenarios after retraining them on experimental data. Essentially, they can serve as pre-trained models that can be fine-tuned for specific applications. Our study opens the door for future developments, including considering complete event topologies and realistic neutrino fluxes. Furthermore, these deep neural network models can be repurposed to simulate related processes by utilizing advanced deep learning techniques such as transfer learning [15].

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section -- leveraging Standard Model symmetries -- can be learned from near-detector data. We then perform a neutrino oscillation analysis with simulated far-detector events, finding that the modeled cross section achieves results consistent with what could be obtained if the true cross section were known exactly. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model parameters with phenomenological constraints. As a concrete example, we focus on a minimal axion model with a global $U(1)$ flavor symmetry. Agents of the learning succeed in finding $U(1)$ charge assignments of quarks and leptons solving the flavor and cosmological puzzles in the Standard Model, and find more than 150 realistic solutions for the quark sector taking renormalization effects into account. For the solutions found by the reinforcement learning-based analysis, we discuss the sensitivity of future experiments for the detection of an axion which is a Nambu-Goldstone boson of the spontaneously broken $U(1)$. We also examine how fast the reinforcement learning-based searching method finds the best discrete parameters in comparison with conventional optimization methods. In conclusion, the efficient parameter search based on the reinforcement learning-based strategy enables us to perform a statistical analysis of the vast parameter space associated with the axion model from flavor.