Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

Spiking neural networks combine analog computation with event-based communication
using discrete spikes. While the impressive advances of deep learning are enabled by …

Hybrid dynamical systems, ie, systems that have both continuous and discrete states, are
ubiquitous in engineering but are difficult to work with due to their discontinuous transitions …

Dynamic optimization problems are typically quite challenging for large-scale applications.
Even more challenging are on-line applications with demanding real-time constraints. This …

Hybrid (discrete/continuous) systems exhibit both discrete state and continuous state
dynamics which interact to such a significant extent that they cannot be decoupled and must …

The design of an effective plant‐wide control strategy is a key challenge for the development
of future continuous pharmaceutical processes. This article presents a case study for the …

In this paper, we use the calculus of variations to derive a sensitivity analysis for ordinary
differential equations with events. This swee** gradient method (SGM) requires a forward …

Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-
constrained systems due to their expressivity and low computational requirements …

Many engineering tasks can be formulated as dynamic optimization or open-loop optimal
control problems, where we search a priori for the input profiles to a dynamic system that …

Large disturbances in power systems often initiate complex interactions between continuous
dynamics and discrete events. The paper develops a hybrid automaton that describes such …