Eco Wave Global AB, a leading onshore wave energy technology company, has announced the completion of the first phase of its AI-powered digital twin development programme with Germany-based AI engineering GmbH.
The milestone represents an important step in Eco Wave Power’s strategy to make wave energy increasingly intelligent, predictive, and scalable.
Phase 1 created a physics-based digital model of how ocean waves interact with Eco Wave Power’s proprietary floaters. The work included simulations of wave loading, floater movement, structural forces and potential energy generation under varying sea conditions, together with initial machine-learning capabilities for energy and load forecasting.
Importantly, the project also evaluated how the model can be adapted to future sites with different wave conditions – potentially enabling Eco Wave Power to transfer knowledge and engineering insights from one project to the next as it expands globally.
“We are building the digital intelligence around our physical technology,” said Inna Braverman, Founder and CEO of Eco Wave Power.
“Our technology already converts ocean waves into clean electricity. By combining it with digital twins and AI, our goal is to increasingly predict system performance, optimise our technology before deployment and learn faster from every project we build.”
“We believe this can help us improve engineering decisions, accelerate deployment and ultimately create increasingly intelligent wave energy systems as we move toward larger commercial installations.”
Both Eco Wave Power U.S. and AI engineering GmbH are members of the NVIDIA Inception programme, connecting the collaboration to NVIDIA’s ecosys-tem of AI, accelerated computing and digital twin technologies.
As part of the digital twin initiative, the companies are utilising and evaluat-ing NVIDIA Omniverse for digital twin visualisation and simulation work-flows, NVIDIA Warp within the simulation and modelling toolchain, and NVIDIA-accelerated computing infrastructure to support high-fidelity simulation and machine-learning development.
“Wave energy is a particularly challenging engineering problem because it combines complex free-surface fluid dynamics, structural motion, and highly variable environmental conditions. Our goal is to create a physics-based digital representation of Eco Wave Power’s system that links high-fidelity simulation with operational data. This approach allows us to better understand energy capture, structural loading, and system behavior across a wide range of sea states. Over time, combining these physics models with machine learning can support more accurate forecasting, design optimisation, and scalable deployment of wave energy technology,” said PD Dr.-Ing. habil. Stefan Adami, Co-Founder and General Manager of AI engineering GmbH.
With Phase 1 completed, Eco Wave Power and AI engineering have established a roadmap for potential subsequent stages of the digital twin.
Phase 2 is expected to expand the model to Eco Wave Power’s hydraulic and mechanical energy-conversion system.
Phase 3 would connect the digital twin with real-world operational sensors, environmental conditions and actual electricity-generation data.
Phase 4 would introduce a broader machine-learning operational layer designed to support energy forecasting, system-behaviour prediction, and increasingly intelligent optimisation.
“Phase 1 gave us the digital foundation. The next stages are about connecting that foundation to our full energy-conversion system, real-world data and ultimately AI-driven operation,” Braverman concluded.
“Our goal is simple: to make wave energy smarter, easier to scale and increasingly capable of interacting intelligently and securely with the energy infrastructure of the future.”
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