A Universal Physics-Informed Neural Network Architecture via Compressible Maxwell-Navier-Stokes Infofluid Dynamics, Modulo-9 Lattices, and Dirichlet L-Function Regulators

By first prototype in neural networks, with a very simple example in Pytorch, just to check performance.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-16
DOI
https://doi.org/10.5281/zenodo.23423708
Primary Topic
Model Reduction and Neural Networks
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

A Universal Physics-Informed Neural Network Architecture via Compressible Maxwell-Navier-Stokes Infofluid Dynamics, Modulo-9 Lattices, and Dirichlet L-Function Regulators

Herman Jahzeel Martinez Suazo
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

A Universal Physics-Informed Neural Network Architecture via Compressible Maxwell-Navier-Stokes Infofluid Dynamics, Modulo-9 Lattices, and Dirichlet L-Function Regulators

Herman Jahzeel Martinez Suazo
preprint en

Abstract

By first prototype in neural networks, with a very simple example in Pytorch, just to check performance.

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.