Numerical simulation in porous media relies on the discrete representation of equations that have been derived from conservation laws and constitutive relations. In many practical applications, the scope of these equations can be insufficient to realistically describe the dynamics of flow in complex porous media induced by sudden changes in boundary conditions and force terms (e.g., wells, aquifers). Nevertheless, the increasing affordability for collecting and storing large volumes of data is enabling possibilities to gain new insights and discover elusive physical relations missing from existing simulation models. In this presentation, we introduce a framework of combined physics-based and data-driven models, namely Physics-AI (or PhysAI) models, to reconstruct and predict the dynamics of fluid flow in diverse unconventional field scenarios. These models are designed to learn and identify spatiotemporal relationships from monitoring data as well as providing explainable interpretations in the form of differential equations if needed. Hence, the proposed approach differs from other well-known data-driven approaches that strictly rely on black-box solutions. Capabilities of PhysAI models are evaluated for reliably predicting state data (e.g., pressure and saturation) as well as for forecasting multi-well production data on multiphase and couple flow/geomechanics applications involving both synthetic and modest field data requirements. A stochastic optimization approach is coupled with the resulting PhysAI model for generating optimal in-fill, drawdown, and completion design recommendations in different unconventional fields.