SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs
type: Item
instance of: scholarly article
title: SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs
author: Amuthan Arunkumar Ramabathiran
author: Prabhu Ramachandran
published in: Journal of Computational Physics
publication date: 2022-04-29T00:00:00Z
full work available at URL: https://arxiv.org/abs/2102.13037
Mathematics Subject Classification ID: 68Txx
Mathematics Subject Classification ID: 65Nxx
Mathematics Subject Classification ID: 65Mxx
zbMATH DE Number: 7515856
zbMATH Keywords: physics-based neural networks
zbMATH Keywords: sparse neural networks
zbMATH Keywords: interpretable machine learning
zbMATH Keywords: partial differential equations
zbMATH Keywords: meshless methods
zbMATH Keywords: numerical methods for PDEs
Wikidata QID: Q114163429
describes a project that uses: Gmsh
describes a project that uses: Matplotlib
describes a project that uses: DGM
describes a project that uses: automan
describes a project that uses: MscaleDNN
describes a project that uses: FEniCS
describes a project that uses: Pyclaw
describes a project that uses: FastDer++
describes a project that uses: PySPH
describes a project that uses: Adam
describes a project that uses: PDE-Net
describes a project that uses: MayaVi
describes a project that uses: NumPy
describes a project that uses: PyWENO
describes a project that uses: Mayavi
describes a project that uses: PyTorch
describes a project that uses: DeepXDE
MaRDI profile type: Publication
OpenAlex ID: W3184364599
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DOI: 10.1016/J.JCP.2021.110600
description: scientific article