Trending Research
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
lululxvi/deepxde
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Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering.
Computational Physics
Disorder in Andreev reflection of a quantum Hall edge
autonomousvision/stylegan_xl
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We find the statistical distribution of the conductance and its dependence on electron density, magnetic field, and temperature.
Mesoscale and Nanoscale Physics Superconductivity
Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
Tensor product state (TPS) based methods are powerful tools to efficiently simulate quantum many-body systems in and out of equilibrium.
Strongly Correlated Electrons
The geometry of nonlinear least squares with applications to sloppy models and optimization
Parameter estimation by nonlinear least squares minimization is a common problem with an elegant geometric interpretation: the possible parameter values of a model induce a manifold in the space of data predictions.
Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability
SeQUeNCe: A Customizable Discrete-Event Simulator of Quantum Networks
We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories.
Quantum Physics
Variational Quantum Pulse Learning
Inspired by the promising performance of VQC, in this paper we propose variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks.
Quantum Physics
Modified Shallow Water Equations for significantly varying seabeds
The novel system is a non-dispersive non-hydrostatic extension of the classical Saint-Venant equations.
Classical Physics Analysis of PDEs Numerical Analysis Computational Physics Fluid Dynamics
Uncertainty propagation with functionally correlated quantities
Many uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities.
Data Analysis, Statistics and Probability
Wavelet Adaptive Proper Orthogonal Decomposition for Large Scale Flow Data
The proper orthogonal decomposition (POD) is a powerful classical tool in fluid mechanics used, for instance, for model reduction and extraction of coherent flow features.
Fluid Dynamics Numerical Analysis Numerical Analysis
Data-Driven Collective Variables for Enhanced Sampling
luigibonati/data-driven-CVs
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Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods.
Chemical Physics Computational Physics
