Nouveauté

S_U1™ Empirical foundations Validating and refining the symbolic framework for reality

Par : TrendFusion Hub, N. Sahra
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  • FormatePub
  • ISBN8231485369
  • EAN9798231485369
  • Date de parution20/05/2025
  • Protection num.pas de protection
  • Infos supplémentairesepub
  • ÉditeurWalzone Press

Résumé

Title: Validation and Stabilization of the S_U1T Universal Metric: Bridging AI and Quantum Systems by N. SahraDescription:This volume presents the critical simulation and validation of the S_U1T metric, an innovative framework designed to quantify the interplay between coherence and disorder in both artificial intelligence and quantum entangled systems. Through simulated experiments with entangled qubits and adaptive AI learning rates, the study demonstrates S_U1T's potential as a universal descriptor of complex system dynamics.
Addressing mathematical instability and practical implementation challenges, it proposes a stabilized model ready for empirical testing and peer review. This work lays the groundwork for cross-disciplinary scientific collaboration, inviting replication and refinement to further advance understanding of entropy-driven behaviors across computational and physical domains. The publication includes detailed methodological descriptions, theoretical analysis, and suggestions for future research paths.
Title: Validation and Stabilization of the S_U1T Universal Metric: Bridging AI and Quantum Systems by N. SahraDescription:This volume presents the critical simulation and validation of the S_U1T metric, an innovative framework designed to quantify the interplay between coherence and disorder in both artificial intelligence and quantum entangled systems. Through simulated experiments with entangled qubits and adaptive AI learning rates, the study demonstrates S_U1T's potential as a universal descriptor of complex system dynamics.
Addressing mathematical instability and practical implementation challenges, it proposes a stabilized model ready for empirical testing and peer review. This work lays the groundwork for cross-disciplinary scientific collaboration, inviting replication and refinement to further advance understanding of entropy-driven behaviors across computational and physical domains. The publication includes detailed methodological descriptions, theoretical analysis, and suggestions for future research paths.
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