It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …

Applying concepts and methods of bionics to endow autonomous robots with elegant and
agile mobility just like natural living beings is gradually becoming a hot research topic in …

Optimal transport (OT) theory describes general principles to define and select, among many
possible choices, the most efficient way to map a probability measure onto another. That …

Understanding and predicting molecular responses in single cells upon chemical, genetic or
mechanical perturbations is a core question in biology. Obtaining single-cell measurements …

Deep networks are commonly used to model dynamical systems, predicting how the state of
a system will evolve over time (either autonomously or in response to control inputs) …

In this paper, we present a novel and principled approach to learn the optimal transport
between two distributions, from samples. Guided by the optimal transport theory, we learn …

We propose a new approach to model the collective dynamics of a population of particles
evolving with time. As is often the case in challenging scientific applications, notably single …

Wasserstein gradient flows provide a powerful means of understanding and solving many
diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of …

Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …

Buildings, as major energy consumers, can provide great untapped demand response (DR)
resources for grid services. However, their participation remains low in real-life. One major …