Downloads provided by UsageCountsNeuroDiffEq: A Python package for solving differential equations with neural networks
Copyright policy ) Feiyu Chen; David Sondak; Pavlos Protopapas; Marios Mattheakis; Shuheng Liu 0001; Devansh Agarwal; Marco Di Giovanni 0001;
NeuroDiffEq: A Python package for solving differential equations with neural networks
NeuroDiffEq is a Python package built with PyTorch that uses artificial neural networks (ANNs) to solve ordinary and partial differential equations (ODEs and PDEs). The paper explains the motivation for building this package and gives a high-level description of the implementation.
Italy
- West Virginia University United States
- University of Virginia United States
- Harvard University, Cambridge, MA, USA United States
- HARVARD UNIVERSITY
- Harvard University
Python package, ordinary differential equations, partial differential equations, Artificial Neural Networks, differential equations, Python package, artificial neural networks, artificial neural networks
Python package, ordinary differential equations, partial differential equations, Artificial Neural Networks, differential equations, Python package, artificial neural networks, artificial neural networks
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).96 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1% views 8 downloads 5 - 8views5downloads
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
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