Technology
Blog

Our collaborative research, Active Learning for Neural PDE Solvers, revolutionizes scientific simulation by cutting data generation costs, improving model accuracy, and accelerating engineering workflows. This breakthrough enables faster PDE‑based predictions, real‑time digital twins, and efficient AI‑driven optimization – transforming how industries solve complex physical systems with smarter, scalable machine‑learning methods.

NEC introduces SYNQA, a synthetic data approach that enables transparent, trustworthy question answering by training compact attribution models that enhance LLM reliability, reduce hallucinations, lower computational costs, and precisely link AI responses to verifiable evidence at scale.

As NEC and NEC Laboratories Europe step onto the global stage at Mobile World Congress 2026 (MWC 2026), we continue to demonstrate leadership in shaping the future of cellular networking. NEC is at the forefront of breakthroughs in vRAN optimization, non-terrestrial networks, AI-driven automation and next generation RAN intelligence.

Learning paradigms designed for multiple domains or tasks, such as multitask learning, continual learning and domain adaptation, aim to reduce the large amount of energy and manual labor needed to retrain machine learning models. In this work, we introduce a domain adaptation approach that exploits learned features from relevant source tasks to reduce the data required for learning the new target task.