Trending Research
Renormalized Classical Theory of Quantum Magnets
We derive a renormalized classical spin (RCS) theory for $S > 1/2$ quantum magnets by constraining a generalized classical theory that includes all multipolar fluctuations to a reduced CP$^1$ phase space of dipolar SU($2$) coherent states.
Strongly Correlated Electrons
ELECTRODE: An electrochemistry package for atomistic simulations
Here we present a feature-rich CPM implementation, called ELECTRODE, for the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), which includes a constrained charge method and a thermo-potentiostat.
Chemical Physics Computational Physics
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
By using advanced gradient boosted decision trees in combination with ensembling techniques and an extended set of features, we significantly improve the performance of weakly supervised methods for anomaly detection at the LHC.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
GWSpace: a multi-mission science data simulator for space-based gravitational wave detection
In this paper, we introduce \texttt{GWSpace}, a package that can simulate the joint detection data from TianQin, LISA, and TaiJi.
General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
KomaMRI.jl: An Open-Source Framework for General MRI Simulations with GPU Acceleration
Results: Koma was compared to two well-known open-source MRI simulators, JEMRIS and MRiLab.
Medical Physics
An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2. 3 Mpixels.
Mesoscale and Nanoscale Physics
The rise of data-driven weather forecasting
A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.
Atmospheric and Oceanic Physics
PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
Teoroo-CMC/PiNN
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Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials.
Computational Physics Disordered Systems and Neural Networks Chemical Physics
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
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
Jianxun-Wang/LabelFree-DNN-Surrogate
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Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system solved by computers.
Computational Physics
