Their paper, entitled “Assessment of Three Deep Reinforcement Learning Algorithms for District Heating Network Optimal Control”, demonstrates how the optimization problem of district heating network control can be reformulated as a policy learning task, by coupling an autonomous agent with a digital twin of the network.

The work addresses the impact of domain walls on the thermal conductivity in ferroelectric and ferroelastic materials.

A new framework for the common works between CSTB and CETHIL

Personal protective equipements against Covid-19 are fabricated by 3D printing teams at INSA, in particular at CETHIL

It covers research projects at CETHIL and a wide variety of scientific disciplines ranging from engineering to digital, including the humanities and social sciences.