Explores mathematical modelling outcomes in complex multivariate data, highlighting the discovery process.
THEK Research Institute · Nested Formula Framework (NFF) A Hierarchical Framework for Mathematical Discovery Through Recursive Coefficient Nesting The Nested Formula Framework (NFF) introduces a fundamentally different approach to mathematical modelling. Rather than fitting a single global equation across all variables simultaneously, NFF incorporates variables one at a time through a recursive process in which the coefficients obtained at each level become the dependent quantities modelled at the next. The result is a hierarchy of nested equations that reconstructs complex phenomena progressively, keeping every parameter physically traceable and mathematically justified at every step. NFF is not a black box. It is a transparent, interpretable framework in which the investigator retains full control over each modelling decision — selecting the functional family that best fits the observed behaviour of the data at each level, from linear and exponential to logarithmic, Gaussian, Gumbel and beyond. This level-by-level inspection is precisely what current machine learning and symbolic regression methods cannot offer. The framework derives a central structural result — the Fundamental Law Nc = sn — which was not postulated but emerged from systematic derivation across six primary functional families. It further demonstrates that hybrid architectures combining different families across levels generate new functional structures naturally: power laws, double exponentials, iterated logarithms and cascading extreme-value distributions arise without prior assumption. Applications span seismic attenuation modelling, climate sensitivity analysis, financial yield curves, epidemiological growth dynamics and materials science — anywhere that understanding the structure of a phenomenon matters as much as predicting it. Researchers working with complex multivariate data are invited to explore NFF as a tool for equation discovery that is both computationally tractable and scientifically meaningful. M.Sc. Marcelo Moncayo Theurer THEK Research Institute · 2026 CC BY 4.0 Zenodo · THEK-WP-NFF-2025-001 . DOI : 20776659
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