τ -Diag2Diag: A Causal 5D Generalization of Diag2Diag with Learned Delay Spectra
Year: 2025
Type: article
Abstract: τ -Diag2Diag extends the Princeton Diag2Diag framework (Jalalvand et al., Nature Commu-nications 2025) by introducing a physically interpretable five-dimensional causal delay-field.Instead of relying on implicit memory in recurrent or transformer layers, τ -Diag2Diag learns ex-plicit Sum-of-Exponentials (SOE) delay kernels κτ,j→A(t) that represent the causal influence andmemory depth of each diagnostic channel. This yields both superior reconstruction accuracy anda direct mapping between diagnostics and their characteristic delay timescales.
Author: Masarratbakhsh, bahman
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7085192740
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