activity
20172025
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 838 across the 18 of their papers we have counts for

collaborators
Showing cond-mat.softShow all

10 papers · 1 filter

cond-mat.soft2024★ 1 cited

Cornerstones are the Key Stones: Using Interpretable Machine Learning to Probe the Clogging Process in 2D Granular Hoppers

Jesse M. Hanlan, Sam Dillavou, Andrea J. Liu +1

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the out…

cond-mat.soft2024★ 1 cited

The Equation of Motion for Taut-Line Buzzers

Alexander J. Gerra, Courtney C. Jones, Sam Dillavou +4

Equations of motion are developed for the oscillatory rotation of a disk suspended between twisted strings kept under tension by a hanging mass, to which additional forces may be a…

cond-mat.soft2023

Stochastic dynamics of granular hopper flows: a configurational mode controls the stability of clogs

David Hathcock, Sam Dillavou, Jesse M. Hanlan +2

Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarti…

cond-mat.soft2023★ 34 cited

Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial

Sam Dillavou, Benjamin D Beyer, Menachem Stern +3

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fas…

cond-mat.soft2022★ 1 cited

Aqueous foams in microgravity, measuring bubble sizes

Marina Pasquet, Nicolo Galvani, Olivier Pitois +9

The paper describes a study of wet foams in microgravity whose bubble size distribution evolves due to diffusive gas exchange. We focus on the comparison between the size of bubble…

cond-mat.soft2022★ 28 cited

Desynchronous Learning in a Physics-Driven Learning Network

Jacob F Wycoff, Sam Dillavou, Menachem Stern +2

In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network (ANN)…

cond-mat.soft2020★ 6 cited

Beyond Quality and Quantity: Contact Distribution Encodes Frictional Strength

Sam Dillavou, Yohai Bar Sinai, Michael P Brenner +1

Classically, the quantity of contact area between two bodies is considered a proxy for the force of friction. However, bond density across the interface - quality of contact…

cond-mat.soft2019★ 12 cited

Influences of microcontact shape on the state of a frictional interface

Tom Pilvelait, Sam Dillavou, Shmuel M. Rubinstein

The real area of contact of a frictional interface changes rapidly when the normal load is altered, and evolves slowly when normal load is held constant, aging over time. Tradition…

cond-mat.soft2019★ 24 cited

Shear Destruction of Frictional Aging and Memory

Sam Dillavou, Shmuel M Rubinstein

We simultaneously measure the static friction and the real area of contact between two solid bodies. Under static conditions both quantities increase logarithmically in time, a phe…

cond-mat.soft2017★ 71 cited

Nonmonotonic Aging and Memory in a Frictional Interface

Sam Dillavou, Shmuel M. Rubinstein

We measure the static frictional resistance and the real area of contact between two solid blocks subjected to a normal load. We show that following a two-step change in the normal…