1 citations · 1 across the 4 of their papers we have counts for
6 papers
How to Train Your Resistive Network: Generalized Equilibrium Propagation and Analytical Learning
Jonathan Lin, Aman Desai, Frank Barrows +1
Machine learning is a powerful method of extracting meaning from data; unfortunately, current digital hardware is extremely energy-intensive. There is interest in an alternative an…
A unifying approach to self-organizing systems interacting via conservation laws
Frank Barrows, Guanming Zhang, Satyam Anand +5
We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, bio…
Machine learning models for Si nanoparticle growth in nonthermal plasma
Matt Raymond, Paolo Elvati, Jacob C. Saldinger +3
Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant chall…
Coupled-mode theory for astrophotonics
Jonathan Lin
Coupled-mode theory (CMT) is a powerful tool for simulating near-harmonic systems. In telecommunications, variations of the theory have been used extensively to study waveguides, b…
Uncontrolled learning: co-design of neuromorphic hardware topology for neuromorphic algorithms
Frank Barrows, Jonathan Lin, Francesco Caravelli +1
Hardware-based neuromorphic computing remains an elusive goal with the potential to profoundly impact future technologies and deepen our understanding of emergent intelligence. The…
Memristive Linear Algebra
Jonathan Lin, Frank Barrows, Francesco Caravelli
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific a…