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Found 8 papers, 2 papers with code
Exploring the impact of automated vehicles lane-changing behavior on urban network efficiency
While automated vehicle (AV) research has grown steadily in recent years, the impact of automated lane changing behavior on transportation systems remains a largely understudied topic.
Learning ground states of gapped quantum Hamiltonians with Kernel Methods
Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem.
Positive-definite parametrization of mixed quantum states with deep neural networks
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with polynomial resources.
From Tensor Network Quantum States to Tensorial Recurrent Neural Networks
We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update.
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks
Efficient sampling of complex high-dimensional probability distributions is a central task in computational science.
Mott insulating states with competing orders in the triangular lattice Hubbard model
We propose a scenario in which time-reversal symmetry-broken states compete with stripy-spin states at lowest temperatures.
Strongly Correlated Electrons
Many-Configuration Markov-Chain Monte Carlo
We propose a minimal generalization of the celebrated Markov-Chain Monte Carlo algorithm which allows for an arbitrary number of configurations to be visited at every Monte Carlo step.
Computational Physics Disordered Systems and Neural Networks Statistical Mechanics Strongly Correlated Electrons
Neural networks trained with WiFi traces to predict airport passenger behavior
Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two.
