AAC Physics: Structure-Preserving Framework for Exact, Reversible Discrete-Time Convolutions
Abstract
Memory-dependent discrete-time systems arise across physics, engineering, and machine learning—from viscoelasticity and control to differentiable programming. Standard recursive approaches accelerate convolution computation but suffer from drift, unstable backward passes, and numerical errors. (note: this paper was written using AI based on MY idea)
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Academic Categories
Algorithms
Formal Sciences > Computer Science > Theory of Computation > Algorithms
Machine Learning
Formal Sciences > Computer Science > Artificial Intelligence > Machine Learning
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