<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>obasanmii.r-universe.dev</title><link>https://obasanmii.r-universe.dev</link><description>Recent package updates in obasanmii</description><generator>R-universe</generator><image><url>https://github.com/obasanmii.png</url><title>R packages by obasanmii</title><link>https://obasanmii.r-universe.dev</link></image><lastBuildDate>Sun, 01 Nov 2026 22:09:58 GMT</lastBuildDate><item><title>[obasanmii] pinnr 0.1.1</title><author>obasanmisamson722@gmail.com (Samson Obasanmi)</author><description>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)
&lt;doi:10.1016/j.jcp.2018.10.045&gt;. 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.</description><link>https://github.com/r-universe/obasanmii/actions/runs/41362738343</link><pubDate>Sun, 01 Nov 2026 22:09:58 GMT</pubDate><r:package>pinnr</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://obasanmii.r-universe.dev</r:repository><r:upstream>https://github.com/Obasanmii/pinnr</r:upstream><r:article><r:source>pinnr.Rmd</r:source><r:filename>pinnr.html</r:filename><r:title>Getting started with pinnr</r:title><r:created>2026-10-31 01:18:43</r:created><r:modified>2026-10-31 01:18:43</r:modified></r:article></item></channel></rss>