PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
Optimizing eigenvalues of the adjacency matrix in linear reservoir computers yields better training and test performance than random linear reservoirs and often beats comparable nonlinear ones.
Divide-and-conquer modeling using scenario-specific techniques reaches a public score of 79.63 on the CTF-4-Science Lorenz benchmark.
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Optimizing the Network Topology of a Linear Reservoir Computer
Optimizing eigenvalues of the adjacency matrix in linear reservoir computers yields better training and test performance than random linear reservoirs and often beats comparable nonlinear ones.