There is a wide variety of applications in which we may need knowledge of a transient fluid flow, but our only information about a flow disturbance comes from a few noisy sensors. For example, small flight vehicles, targeted for many emerging applications, are more agile but also more strongly affected by unexpected disturbances (‘gusts’) than larger vehicles. The nonlinear aerodynamics of these gust encounters remains a principal challenge in controlling the vehicle’s flight. In particular, any such flight control strategy is generally more effective if it can rely on an estimation of the vehicle’s current flow state from available sensors, but also, be aware of uncertainty of this state. In this talk, I will discuss the dynamic estimation of disturbed flows from limited sensor data and the control of the flow with deep learning strategies. In the first part, I will discuss aspects of the flow estimation problem within the framework of sequential filtering, which allows us to easily assimilate streaming sensor data into dynamical models for the flow. In particular, I will show how we can learn low-order models from data that fit nicely into this framework and enable fast flow estimation. In the examples I will show, we use the estimation framework to predict the fluid dynamics of a separated aerodynamic flow subjected to a gust, relying on a small number of surface pressure measurements to inform the model of the gust. I will then discuss the use of deep reinforcement learning to develop strategies for the mitigation of gust encounters, based on available sensor data.