AMPERA AND IMPERIAL COLLEGE LONDON PARTNER TO DEVELOP PHYSICS-GROUNDED AI PLATFORM FOR ADVANCED ENERGY SYSTEMS
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Proprietary NeuralTwin⢠technology platform designed to accelerate engineering and optimize performance

PALM BEACH GARDENS, Fla., Oct. 27, 2026 /PRNewswire/ -- Advanced energy company AMPERA today announced a strategic ~$9 million / £7 million multi-year partnership with global research leader Imperial College London to develop a physics-grounded, digital twin platform known as NeuralTwin.

The collaboration brings together AMPERA's technology platform and Imperial College London's world-class expertise in AI, engineering, computational science and research. Together, the organizations will explore new approaches to creating intelligent digital representations that can learn from data, model dynamical systems and generate real-time insights. The program addresses methods to design, assess, optimize and validate energy systems to support faster, more cost-effective rapid deployment.

Unlike conventional digital twins, which can depend heavily on predefined models and computationally intensive simulations, NeuralTwin combines first-principles physics with AI-based surrogates to accelerate engineering, optimize performance, improve safety, support licensing and ultimately enable increasingly predictive, autonomous operation.

"This collaboration will supercharge every aspect of our technology development," said AMPERA Founder and CEO Brian Matthews. "Imperial College London is a world-leading university, and the technology has the potential to transform how complex systems are understood, monitored, optimized and managed. From licensing to engineering analysis to autonomous operations, we see NeuralTwin as being a marketplace differentiator."

The collaboration will provide a platform for continued research into the convergence of AI, advanced modeling, and real-time computational systems. The wider impact will reduce time, cost and technical risk associated with moving from initial concept to deployable, manufacturable and licensable energy systems.

"We are passionate about translating cutting-edge academic research into the commercial sector where it can accelerate the development of next-generation energy systems," said Professor Christopher Pain, at Imperial College London. "This collaboration brings together two organizations focused on neural physics for processing, analyzing and executing ultra-complex operations in the safest, quickest way possible."

"NeuralTwin reflects an important next step in bringing together scientific machine learning, digital twins, and high-fidelity computational methods," said Dr. Jiansheng Xiang, Co-Investigator at Imperial College London.

About AMPERA

AMPERA is an advanced energy technology company developing scalable power solutions for data center, defense, industrial and maritime applications. Through its PowerNOW. NuclearNEXT.⢠strategy, AMPERA combines advanced manufacturing, artificial intelligence and next-generation energy technologies to deliver reliable, factory-built power systems. Its proprietary Integrated Energy Architecture⢠supports waste heat recovery, conventionally fueled power generation and advanced subcritical nuclear energy, creating a seamless pathway to the future of clean, abundant energy. Learn more at www.amperaglobal.com.

About Imperial College London

Imperial College London is a world-leading university focused on science, engineering, medicine, and business. Founded in 1907, its research and education address some of the world's most significant challenges through interdisciplinary collaboration and innovation. Learn more at https://www.imperial.ac.uk/.

Media Contacts

AMPERA

[email protected]

Imperial College London

David Silverman

[email protected]

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<p>Proprietary NeuralTwin™ technology platform designed to accelerate engineering and optimize performance PALM BEACH GARDENS, Fla., Oct. 27, 2026 /PRNewswire/ -- Advanced energy company AMPERA today announced a strategic ~$9 million / £7 million multi-year partnership with global research...</p>

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