CS

Quantum Computing: Machine Learning with Emphasis on Quantum Boltzmann Machines

Studies of Quantum Dots using Neural Networks and Coupled Cluster

Electrostatics in Mesoscale Simulations of Biological Membranes using the Hybrid Particle-Field Approach

Evaluating the Phase Behaviour of Silica Modeled by the Vashishta Potential Using Free Energy Methods

Studying Place Cell Formation and Remapping in an Artificial Neural Network Model

Predicting static friction in a molecular dynamic system using machine learning

Bayesian neural network estimation of next-to-leading order cross sections

A machine learning approach to understanding depression and anxiety in new mothers

Towards predicting Harmful Conspiracies through Phase Transitions in Complex Interaction Networks

Applications of the Yang-Mills gradient flow to topological observables in QCD

Solving the Many-Electron Problem Using Neural Networks and Variational Monte Carlo

Characterization of Cardiac Cellular Dynamics Using Physics-informed Neural Networks