FlexLab

Fri, 01 Aug 2025 00:00:00 +0000

FlexLab is an AI innovation lab that creates sustainable and societal impact by applying advanced AI technologies to enable flexible electricity consumption and ensure that this flexibility can be used to resolve congestion in medium- and low-voltage power grids. FlexLab achieves this by providing a secure environment in which startups and SMEs can test and validate their flexible energy solutions together with value-chain partners.

In addition, FlexLab develops expertise in core AI technologies required for flexible electricity consumption and evaluates these technologies through short-cycle innovation trajectories. This approach helps address grid congestion effectively, improve grid reliability and stability, and contribute to the energy transition toward a CO2-neutral Netherlands by 2050.

An initial prototype will be developed to apply Bayesian networking and deep reinforcement learning to behind-the-meter energy control. Bayesian networking increases the reliability of behind-the-meter energy management, and this work is carried out in collaboration with Zympler (formerly known as Simpl.Energy).

BayesBrain

Thu, 01 Apr 2021 00:00:00 +0000

Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems. Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer, introducing a fundamentally new paradigm of AI computing. In this high-risk high-gain project, we will combine an in-silico Bayesian control agent (BCA) with neural tissue hosted by a microfluidic Brain-on-Chip (BoC) that together form a hybrid learning system capable of solving real-world AI problems.

All computation and communication inside and between the BCA and BoC will be governed by the Free Energy Principle, which is both the leading neuroscientific theory for describing biological neuronal processes and supports a variational Bayesian machine learning interpretation. We will start by developing a pure silicon-based BCA that learns to balance an inverted pendulum, implemented by free energy minimization on a factor graph. Next, we will replace successively larger parts of the factor graph with biological neural circuits of a microfluidic multi-compartment BoC device. The biological network will be trained by electrical stimulation orchestrated by the synthetic Bayesian agent. For the communication between these two units, we will design and realize a novel communication protocol making use of existing software being applied in readout and event sorting for Calcium imaging and multi-electrode array data, such as MEAViewer, CALIMA, NetCal and SpikeHunter. By upscaling the number of replaced sub-circuits, we aim to provide a proof-of-concept and to lay the basis for ultra-low power hybrid brain-on-chip AI computing.

This position is supported by the Exploratory Multidisciplinary AI Research Program of the Eindhoven Artificial Intelligence Systems Institute.

Energy | BIASlab

FlexLab

BayesBrain