Eco Wave Power’s U.S. subsidiary has entered into an agreement with Germany-based AI engineering to develop an AI-powered digital twin platform for its wave energy technology.
The companies have begun Phase 1 of the Eco Wave Power – Digital Twin & Energy-Aware Computing project, combining advanced physics-based simulation with Eco Wave Power’s real-world engineering, wave and operational data.
This first phase will focus on digitally modeling how ocean waves interact with Eco Wave Power‘s proprietary floaters. Using AI engineering’s PAMICS simulation technology, the companies will evaluate floater behavior, structural loads and theoretical energy input under different sea conditions.
The plan is to compare simulated results with real-world sensor measurements and begin developing machine-learning capabilities for forecasting energy yield and system loads.
“For us, AI is not simply a software layer that we add to our technology—it is an opportunity to fundamentally improve how we understand, design, predict and ultimately operate wave energy systems,” said Inna Braverman, Founder and CEO of Eco Wave Power.
“Phase 1 of this collaboration is therefore very practical. We will model wave loading, floater behavior, structural forces and theoretical energy input, while beginning to develop machine-learning capabilities for forecasting loads and energy yield. Importantly, we will also examine how those models can be transferred to new sites with different wave conditions.”
According to the wave energy developer, a key objective is to determine how the digital twin can be adapted to different project locations, potentially allowing Eco Wave Power to transfer engineering knowledge and operational insights more efficiently as it expands into new markets.
Eco Wave Power currently has projects and development activities across the U.S., Europe and Asia, including its grid-connected project at Jaffa Port in Israel, its Port of Los Angeles pilot, and projects under development in Portugal, Taiwan and India.
“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,” said Stefan Adami, Co-Founder and General Manager of AI engineering.
“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 optimization, and scalable deployment of wave energy technology.”
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