Title: Dynamical Algorithm for Wavefunction Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel dynamical algorithm for wavefunction optimization designed to address the challenges inherent in modeling complex, dynamic systems. The algorithm leverages an adaptive wavefunction distribution, dynamically adjusting the representation of a system's state to enhance precision and model fidelity. This approach overcomes limitations of traditional optimization techniques, particularly in scenarios involving high dimensionality and non-linear relationships within the system's dynamics. The core mechanism centers around a feedback loop that iteratively refines the wavefunction representation based on the system's state evolution, leading to improved model accuracy. The resulting algorithm offers a robust and efficient method for parameter tuning and model refinement, offering a significant advancement in computational modeling. The focus is on enhancing the precision of the model, specifically in systems characterized by intricate, time-varying processes. The algorithm's design emphasizes adaptability and minimizes the reliance on computationally intensive optimization methods, making it suitable for a broader range of applications.

DOI: 10.5281/zenodo.22185104

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