In this research project, the team will develop and apply a suite of deep learning models and tools to advance the understanding of the initiation of solar flares, and provide near real-time flare forecasting. There are five interrelated tasks. (1) They will develop a convolutional neural network with attention mechanisms for inverting GST Stokes profiles to vector magnetograms with high efficiency and reduced noise. (2) Using a Bayesian convolutional network with uncertainty quantification, they will trace the fibril and loop structures of chromospheric observations to provide an assessment of magnetic fields in the chromosphere, which is crucial in 3D magnetic field extrapolations. (3) They will train generative adversarial networks (GANs) using simultaneous NASA Solar Dynamics Observatory (SDO) and GST magnetograms to create higher-resolution, larger field-of-view data, which are critical to derive flow fields in flare-producing solar active regions. (4) They will train a new GAN model, using SDO vector magnetograms and Halpha images to derive transverse fields from the NASA Solar Heliospheric Observatory line-of-sight magnetograms. Therefore, the availability of vector magnetograms is extended to two solar cycles. (5) They will develop a new encoder-decoder bidirectional long short-term memory network with attention mechanisms to carry out near real-time flare prediction and evaluate the most critical magnetic parameters relevant to flare initiations. With these data and tools, they will address the following two key science questions: (i) With consistent high-resolution observations, what roles do the evolution of magnetic fields and flow fields play in storing energy and triggering solar flares? (ii) What is the quantitative assessment of flare prediction with the deep learning-processed data and deep learning-based prediction tools?