arXiv:2501.08561v4 Announce Type: replace Abstract: Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to adapt, and limited in their ability to incorporate explicit domain knowledge. This paper presents ANSR-DT, an adaptive neuro-symbolic framework that unifies temporal anomaly detection, symbolic reasoning, and reinforcement-learning-based decision support within a single digital twin pipeline. ANSR-DT combines a CNN-LSTM model for multivariate pattern recognition with Prolog-based reasoning that co
The increasing complexity of industrial systems and the limitations of purely data-driven AI models are driving demand for more adaptive and explainable solutions for digital twins.
This framework offers a path to more robust, interpretable, and adaptable AI-driven control over critical infrastructure and advanced manufacturing, potentially increasing efficiency and reducing operational risks across industries.
Digital twins can now more effectively incorporate explicit domain knowledge and provide more transparent decision-making, moving beyond opaque black-box AI models towards explainable and adaptive control systems.
- · Industrial IoT providers
- · Smart manufacturing sectors
- · Critical infrastructure operators
- · AI/ML developers specializing in hybrid models
- · Providers of purely black-box AI solutions
- · Industrial systems with high manual oversight burdens
Increased adoption of neuro-symbolic AI in industrial applications for monitoring and optimization.
Improved resilience and efficiency in supply chains and complex operational environments enabled by adaptive digital twins.
Enhanced automation and reduced human intervention in system management, leading to new workforce dynamics and skill requirements.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI