The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

Learning for control of dynamical systems with formal guarantees remains a challenging
task. This paper proposes a learning framework to simultaneously stabilize an unknown …

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

Learned models and policies can generalize effectively when evaluated within the
distribution of the training data, but can produce unpredictable and erroneous outputs on out …

Control certificates based on barrier functions have been a powerful tool to generate
probably safe control policies for dynamical systems. However, existing methods based on …

Neural implicit representations, which encode a surface as the level set of a neural network
applied to spatial coordinates, have proven to be remarkably effective for optimizing …

We provide a systematic investigation of using physics-informed neural networks to compute
Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE) …

While ensuring stability for linear systems is well understood, it remains a major challenge
for nonlinear systems. A general approach in such cases is to compute a combination of a …