104 citations · 543 across the 19 of their papers we have counts for
19 papers
Fast generation of spectrally-shaped disorder, on the sphere
Mathias Casiulis, Stefano Martiniani
The design of disordered point patterns with desirable properties is an exciting and ongoing research endeavor, with applications ranging from materials to computer science. A succ…
Emergent universal long-range structure in random-organizing systems
Satyam Anand, Guanming Zhang, Stefano Martiniani
Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains…
Absorbing state dynamics of stochastic gradient descent
Guanming Zhang, Stefano Martiniani
Stochastic gradient descent (SGD) is a fundamental tool for training deep neural networks across a variety of tasks. In self-supervised learning, different input categories map to…
Unconditional stability of a recurrent neural circuit implementing divisive normalization
Shivang Rawat, David J. Heeger, Stefano Martiniani
Stability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can be seamlessly trained. Traditio…
Transport and Energetics of Bacterial Rectification
Satyam Anand, Xiaolei Ma, Shuo Guo +2
Randomly moving active particles can be herded into directed motion by asymmetric geometric structures. Although such a rectification process has been extensively studied due to it…
ColabFit Exchange: open-access datasets for data-driven interatomic potentials
Joshua A. Vita, Eric G. Fuemmeler, Amit Gupta +5
Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational mate…
Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points
Shivang Rawat, Stefano Martiniani
Stochasticity plays a central role in nearly every biological process, and the noise power spectral density (PSD) is a critical tool for understanding variability and information p…
Fast Generation of Spectrally-Shaped Disorder
Aaron Shih, Mathias Casiulis, Stefano Martiniani
Media with correlated disorder display unexpected transport properties, but it is still a challenge to design structures with desired spectral features at scale. In this work, we i…
Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding
Carmine Anzivino, Mathias Casiulis, Tom Zhang +3
We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jamm…
Play. Pause. Rewind. Measuring local entropy production and extractable work in active matter
Sunghan Ro, Buming Guo, Aaron Shih +5
Time-reversal symmetry breaking and entropy production are universal features of nonequilibrium phenomena. Despite its importance in the physics of active and living systems, the e…
Vicsek Model by Time-Interlaced Compression: a Dynamical Computable Information Density
Andrea Cavagna, Paul M. Chaikin, Dov Levine +3
Collective behavior, both in real biological systems as well as in theoretical models, often displays a rich combination of different kinds of order. A clear-cut and unique definit…
Correlation lengths in the language of computable information
Stefano Martiniani, Yuval Lemberg, Paul M. Chaikin +1
Computable Information Density (CID), the ratio of the length of a losslessly compressed data file to that of the uncompressed file, is a measure of order and correlation in both e…
Quantifying hidden order out of equilibrium
Stefano Martiniani, Paul M. Chaikin, Dov Levine
While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hinder…
Perspective: Energy Landscapes for Machine Learning
Andrew J. Ballard, Ritankar Das, Stefano Martiniani +4
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple soluti…
Monte Carlo sampling for stochastic weight functions
Daan Frenkel, K. Julian Schrenk, Stefano Martiniani
Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number,…
Numerical test of the Edwards conjecture shows that all packings are equally probable at jamming
Stefano Martiniani, K. Julian Schrenk, Kabir Ramola +2
In the late 1980s, Sam Edwards proposed a possible statistical-mechanical framework to describe the properties of disordered granular materials. A key assumption underlying the the…
Structural analysis of high-dimensional basins of attraction
Stefano Martiniani, K. Julian Schrenk, Jacob D. Stevenson +2
We propose an efficient Monte Carlo method for the computation of the volumes of high-dimensional bodies with arbitrary shape. We start with a region of known volume within the int…
Turning intractable counting into sampling: computing the configurational entropy of three-dimensional jammed packings
Stefano Martiniani, K. Julian Schrenk, Jacob D. Stevenson +2
We report a numerical calculation of the total number of disordered jammed configurations of repulsive, three-dimensional spheres in a fixed volume . To make these calcu…
Superposition Enhanced Nested Sampling
Stefano Martiniani, Jacob D. Stevenson, David J. Wales +1
The theoretical analysis of many problems in physics, astronomy and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit brok…