activity
20202026
most citedData-Driven Discovery of Coarse-Grained Equations

31 citations · 52 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

EML Trees Are Universal Approximators

Joe Germany, Elie Abdo, Joseph Bakarji

The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, providing a compositional building block capable of representing elementary function…

cs.LG2026

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models

Stefano Riva, Carolina Introini, Antonio Cammi +13

The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…

cs.LG2025

The Seismic Wavefield Common Task Framework

Alexey Yermakov, Yue Zhao, Marine Denolle +13

Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variabilit…

cs.LG2022★ 3 cited

Dimensionally Consistent Learning with Buckingham Pi

Joseph Bakarji, Jared Callaham, Steven L. Brunton +1

In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems. Given measurement variables an…

cs.LG2022★ 12 cited

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders

Joseph Bakarji, Kathleen Champion, J. Nathan Kutz +1

A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem pro…