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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "pinnr" in publications use:'
type: software
license: MIT
title: 'pinnr: Physics-Informed Neural Networks in Native R with ''torch'''
version: 0.1.1
abstract: Fits physics-informed neural networks (PINNs) that solve ordinary and partial
  differential equations and estimate unknown model parameters from data. Networks
  are trained by penalising the equation residual, boundary and initial conditions,
  and misfit to observations, following Raissi, Perdikaris and Karniadakis (2019)
  <https://doi.org/10.1016/j.jcp.2018.10.045>. Everything runs in native R on top
  of 'torch', with no 'Python' dependency, and ordinary differential equation models
  written as 'deSolve'-style derivative functions can be used directly.
authors:
- family-names: Obasanmi
  given-names: Samson
  email: obasanmisamson722@gmail.com
  orcid: https://orcid.org/0009-0009-6935-5479
repository: https://obasanmii.r-universe.dev
repository-code: https://github.com/Obasanmii/pinnr
commit: 1d900c81efe9b4c9316db3f09f04fe1329f677b1
url: https://obasanmii.github.io/pinnr/
date-released: '2026-11-01'
contact:
- family-names: Obasanmi
  given-names: Samson
  email: obasanmisamson722@gmail.com
  orcid: https://orcid.org/0009-0009-6935-5479