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.
