activity
20182025
most citedRestricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries

104 citations · 324 across the 14 of their papers we have counts for

collaborators

14 papers

eess.IV2025★ 4 cited

Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations

Cedric Lim, Corneel Casert, Arthur R. C. McCray +4

Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-w…

cond-mat.stat-mech2025

Benchmark control problems in nonequilibrium statistical mechanics

Stephen Whitelam, Corneel Casert, Megan Engel +1

We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stocha…

cond-mat.stat-mech2024

Nonlinear thermodynamic computing out of equilibrium

Stephen Whitelam, Corneel Casert

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of…

cond-mat.mes-hall2024★ 24 cited

Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth

Leonardo Sabattini, Annalisa Coriolano, Corneel Casert +7

Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable pro…

cond-mat.stat-mech2024

Learning protocols for the fast and efficient control of active matter

Corneel Casert, Stephen Whitelam

We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in t…

cond-mat.stat-mech2022★ 1 cited

Learning stochastic dynamics and predicting emergent behavior using transformers

Corneel Casert, Isaac Tamblyn, Stephen Whitelam

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of th…