This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and
other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of …

The Lie-Trotter formula, together with its higher-order generalizations, provides a direct
approach to decomposing the exponential of a sum of operators. Despite significant effort …

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

Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …

Over the last few years, extraordinary advances in experimental and theoretical tools have
allowed us to monitor and control matter at short time and atomic scales with a high degree …

For infinitesimal learning rates, stochastic gradient descent (SGD) follows the path of
gradient flow on the full batch loss function. However moderately large learning rates can …

We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-
source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently …

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that
capture the dynamics of physical systems from observed trajectories. An SRNN models the …

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training
continuous normalizing flows on manifolds. Existing methods for generative modeling on …