Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

Abstract Purpose of Review The computation methods for modeling, controlling, and
optimizing the transforming grid are evolving rapidly. We review and systemize knowledge …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …

The ushering in of the big-data era, ably supported by exponential advances in computation,
has provided new impetus to data-driven control in several engineering sectors. The rapid …

We present differentiable predictive control (DPC), a method for offline learning of
constrained neural control policies for nonlinear dynamical systems with performance …

Neural ordinary differential equations (NODE) have been recently proposed as a promising
approach for nonlinear system identification tasks. In this work, we systematically compare …

A review of current trends in scientific computing reveals a broad shift to open-source and
higher-level programming languages such as Python and growing career opportunities over …

The problem of synthesizing stochastic explicit model predictive control policies is known to
be quickly intractable even for systems of modest complexity when using classical control …

The dynamics-aware economic dispatch (DED) problem embeds low-level generator
dynamics and operational constraints to enable near real-time scheduling of generation …

We present differentiable predictive control (DPC), a method for learning constrained neural
control policies for linear systems with probabilistic performance guarantees. We employ …