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Found 7 papers, 3 papers with code
Virtual Scientific Companion for Synchrotron Beamlines: A Prototype
In this work, we introduce the prototype of virtual scientific companion (VISION) and demonstrate that it is possible to control basic beamline operations through natural language with open-source language model and the limited computational resources at beamline.
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity.
Multilevel-in-Layer Training for Deep Neural Network Regression
A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting.
Learning optimal multigrid smoothers via neural networks
Multigrid methods are one of the most efficient techniques for solving linear systems arising from Partial Differential Equations (PDEs) and graph Laplacians from machine learning applications.
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries.
A non-perturbative approach to computing seismic normal modes in rotating planets
A Continuous Galerkin method-based approach is presented to compute the seismic normal modes of rotating planets.
Computational Physics Earth and Planetary Astrophysics Geophysics 86-08, 86-04, 85-04, 85-08, 85-10, 15A18, 65N25, 65N30
The Eigenvalues Slicing Library (EVSL): Algorithms, Implementation, and Software
This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems.
Numerical Analysis
