3 papers
stat.ML2026
PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
Charuka D. Wickramasinghe, Krishanthi C. Weerasinghe, Pradeep K. Ranaweera +1
Physics-Informed Neural Networks (PINNs) integrate machine learning with differential equations to solve forward and inverse problems while ensuring that predictions adhere to phys…
cs.LG2025
Physics-Informed Neural Network Frameworks for the Analysis of Engineering and Biological Dynamical Systems Governed by Ordinary Differential Equations
Tyrus Whitman, Andrew Particka, Christopher Diers +3
In this study, we present and validate the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dyn…
math.NA2024
A Graded Mesh Refinement for 2D Poisson's Equation on Non Convex Polygonal Domains
Charuka D. Wickramasinghe, Priyanka Ahire
This work delves into solving the two dimensional Poisson problem through the Finite Element Method which is relevant in various physical scenarios including heat conduction, elect…